Pith. sign in

REVIEW 4 major objections 5 minor 46 references

An Improved ResNet50 Model for Predicting Pavement Condition Index (PCI) Directly from Pavement Images

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Adding CBAM attention to ResNet50 lowers pavement PCI prediction error from 70.76% to 58.16% MAPE on a public road-image dataset.

desk verdict A careless benchmark paper whose headline MAPE improvement is not supported by the reported experiment, largely because augmentation appears to precede the train/validation split. read the letter →

arxiv 2504.18490 v1 pith:CIMLDMI3 submitted 2025-04-25 cs.CV

Andrews Danyo , Anthony Dontoh , Armstrong Aboah This is my paper · ORCID
classification cs.CV
keywords PavementConditionIndexPCIpredictionResNet50ConvolutionalBlockAttentionModuleCBAMmechanismimageregressiondistress
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to show that inserting a Convolutional Block Attention Module (CBAM) into a ResNet50 regressor lets a neural network predict the Pavement Condition Index (PCI), a 0-to-100 road-quality score, directly from pavement photographs without separate distress annotations. On a public dataset of 7,704 pavement images from three US cities, the authors report that ResNet50-CBAM reaches a mean absolute percentage error (MAPE) of 58.16%, better than plain ResNet50 (70.76%) and DenseNet161 (65.48%), and the highest coefficient of determination (R² = 0.61). The paper argues that the attention module's sequential channel-then-spatial focus is what drives the improvement, and calls this the first direct image-to-PCI association that needs no human-labeled distress data. The practical stakes are cheaper and less subjective road-condition monitoring for maintenance planning.

What carries the argument

The central object is the Convolutional Block Attention Module (CBAM), a lightweight attention module that computes a channel attention map (two spatial pooling descriptors passed through a shared MLP and a sigmoid) followed by a spatial attention map (channel-wise pooling followed by a 7×7 convolution and a sigmoid). The paper places CBAM within the residual bottleneck blocks of ResNet50, so every stage of feature extraction can re-weight which channels and which image locations matter for the regression. This sequential channel-then-spatial refinement is the mechanism claimed to separate pavement distress from background without explicit annotation.

What would settle it

Look in the validation set for images that are flips or color-jittered versions of training-set images; if any exist, the reported MAPE gap no longer measures generalization. Re-running the experiment with a per-source-image split and recomputing the three MAPE values would settle whether the CBAM advantage survives a clean split.

Watch

Extended reading notes

Core claim

The central claim is that adding CBAM inside the bottleneck blocks of ResNet50 improves regression of PCI from raw top-down pavement images. CBAM first recalibrates channels via average- and max-pooling through a shared MLP, then recalibrates spatial locations via a 7×7 convolution; the paper reports that this feature-refinement step lowers MAPE from 70.76% and 65.48% to 58.16%, with the smallest RMSE and MAE of the three models, and raises R² to 0.61. In the authors' reading, the improvement comes from targeted feature refinement rather than added capacity, because the parameter increase over ResNet50 is small. They also assert that this is the first effort to relate pavement imagery directly to PCI without relying on human-labeled data, and show attention maps that highlight cracks and potholes as evidence the model learns distress-like regions on its own.

Load-bearing premise

The load-bearing premise is that the 90/10 train/validation split happened before augmentation, so no augmented copy of a source pavement photo appears in both sets; if the split was made after augmentation without grouping by source image, the reported validation metrics are inflated and the head-to-head comparison is not a clean generalization test.

Editorial extensions

If this is right

  • If the result holds, transportation agencies can estimate PCI for a road network directly from routine camera images, skipping the manual distress surveys that make current PCI assessment slow and subjective.
  • Attention-based feature refinement, not raw parameter count, becomes the credible explanation for the accuracy gain: CBAM adds few parameters yet produces the largest error reduction, while the heavier DenseNet161 gains little over ResNet50.
  • The model is most reliable on well-maintained pavements (high PCI) and least reliable on heavily degraded ones, so immediate deployment would be safest for screening roads that are likely in good condition, with low-PCI cases flagged for manual review.
  • A physics-informed loss function that encodes how pavements deteriorate over time is a plausible next step the paper proposes to improve low-PCI predictions and generalization to unseen conditions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • My inference: because MAPE divides by true PCI, the reported gap may be carried mostly by low-PCI images; reporting error per PCI band would show whether CBAM genuinely improves the cases most relevant to maintenance.
  • My inference: the 'first image-to-PCI without human labels' claim is best read as first for a CBAM-style self-attention regressor trained only on image-to-PCI pairs, since direct image-to-PCI regression already exists in the literature the paper cites.
  • My inference: a decisive and cheap extension is to ablate channel and spatial attention separately and to re-split the data by source image; if the CBAM advantage disappears under either change, the headline MAPE gap is not robust.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes augmenting ResNet50 with a Convolutional Block Attention Module (CBAM) to predict the Pavement Condition Index (PCI) directly from pavement images, without per-crack annotations. Using the DSPS competition dataset of 7,704 images augmented to 30,816 images, the authors compare ResNet50+CBAM against ResNet50 and DenseNet161 baselines on RMSE, MAE, and MAPE. They report lower errors for the proposed model (e.g., MAPE 58.56% vs 70.76% and 65.48%) and claim that this is the first direct image-to-PCI approach without human-labeled data. The experimental section also includes attention map visualizations and R² scatter plots.

Significance. If the reported improvements are reliable, the paper offers a modest but practically useful checkpoint for automated pavement condition assessment: adding CBAM to a standard CNN gives a simple way to regress PCI from images, with visual interpretability. The use of a public benchmark dataset and the clear task framing are strengths, and the paper explicitly describes its metrics. However, the significance is undercut by load-bearing issues: the training/validation split is described after data augmentation, creating a risk of leakage; the headline "significant" improvement rests on single-run metrics with no error bars or significance testing; and the novelty claim is in tension with the authors' own cited prior work (reference [4], Image2PCI). These issues prevent the current results from supporting the paper's central claims.

major comments (4)
  1. [Section IV.A] The dataset is resized and augmented (color jitter, random horizontal and vertical flips) to 30,816 images, and only then "the dataset was split into training and validation sets" with 90/10. If the split was made on the augmented set without grouping by source image, augmented variants of the same original photo can appear in both training and validation. Since the reported comparison in Table V (MAPE 58.56 vs 70.76 vs 65.48) is computed on the validation set, the entire head-to-head comparison is compromised under leakage. The paper must state explicitly whether the split was performed on the original 7,704 images (or grouped by source) before augmentation; if not, the experiments must be rerun with a proper split.
  2. [Section V / Abstract] The abstract and Section V describe the MAPE/MAE differences as "significantly lower" or "significant," but the results appear to come from a single training run with no standard deviations, confidence intervals, or significance tests. On the 0–100 PCI scale, the MAE difference between ResNet50+CBAM (13.93) and ResNet50 (14.03) is tiny relative to typical run-to-run variability; MAPE is also sensitive to near-zero PCI values. The authors should report at least three runs with mean±std for each metric and perform a pairwise significance test before using the term "significant." Without this, the central claim of superiority is not statistically supported.
  3. [Section II.E] The claim that "this is the first effort to associate pavement imagery directly with the PCI without relying on human-labeled data" is contradicted by reference [4] (Owor et al., Image2PCI), which the authors themselves cite in the Introduction as "a multitask learning framework for estimating pavement condition indices directly from images." Please reconcile this contradiction or revise the novelty claim to be specific about what is actually new relative to that prior work.
  4. [Section V.B] The comparison text states that DenseNet161 has R²=0.56 "compared to our proposed model with an R² value of 0.56," while Section V.A reports R²=0.61 for the proposed model. This internal inconsistency, together with the small differences among R²=0.61, 0.57, and 0.56, does not support the stated conclusion that the proposed model achieves a substantial improvement. The authors should correct the typo and provide uncertainty information for these R² estimates, or the claim of substantial improvement should be tempered.
minor comments (5)
  1. [Section III.C.2, Eq. (3)] The displayed equation for the spatial attention module appears to be a duplicate of the channel attention formula (Eq. 2); it should involve concatenation along the channel axis and a 7×7 convolution, e.g., \(M_S(F)=\sigma(f^{7\times7}([AvgPool(F);MaxPool(F)]))\).
  2. [Header / Title] The manuscript header contains boilerplate from a different IEEE submission (MTT-S Microwave Symposium), including author names, addresses, and grant numbers that do not match the current paper; this material should be removed.
  3. [Acknowledgments] The Acknowledgments section contains a placeholder: "This should be a simple paragraph before the References to thank those individuals and institutions who have supported your work on this article." Please complete or delete this placeholder.
  4. [Tables / Cross-references] Cross-referencing is inconsistent: the results table is labeled Table V but is referred to as "Table 3" and "Table 1" in the text (Sections V and V.B). Use consistent table numbering throughout.
  5. [Section III.D] The description of CBAM insertion is vague ("CBAM is introduced within the bottleneck") without specifying the exact positions relative to the residual blocks, the reduction ratio, or whether CBAM is applied to the input or output feature maps of each block. Provide these details for reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found: the MAPE comparison is an empirical result on external DSPS data, not an identity with the model's inputs.

full rationale

The paper's central claim is an empirical head-to-head comparison: ResNet50+CBAM achieves a MAPE of 58.16% versus 70.76% for ResNet50 and 65.48% for DenseNet161 on the DSPS validation set. Tracing the derivation chain, the proposed model is a standard ResNet50 with CBAM inserted into the bottleneck blocks; the CBAM equations (2) and (3) are the standard channel and spatial attention definitions and contain no dependence on the PCI target. The metrics (MAE, MAPE, RMSE) are defined externally, and the training data are public DSPS pavement images with PCI labels. No fitted parameter is renamed as a prediction, and no result is shown to equal its own input by construction. The many Aboah-group self-citations in the references are contextual literature citations; none is invoked as a uniqueness theorem or as the justification that CBAM improves PCI prediction. The validation set is used for early stopping, which can bias the reported numbers, and the augmentation-before-split order raises a possible data-leakage concern, but those are evaluation-validity issues rather than circularity because the reported metrics are not algebraically forced by the training labels or by a self-citation chain. The acknowledged limitations, such as weaker performance on low-PCI edge cases, are empirical and do not indicate circular reasoning. Therefore no circular step meeting the required evidentiary standard was found.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The paper's claim is an empirical comparison, not a derivation. What the reader pays for upstream: the assumed correctness of the PCI labels, the assumption that augmentation before the split does not leak, and several unreported training choices (scheduler, early stopping, reduction ratio) that the reported metrics depend on. No new entities are introduced; the model is a standard composition of ResNet50 and CBAM.

free parameters (6)
  • batch size = 32
    Section IV.A sets batch size 32 with no ablation; it affects training dynamics and the reported metrics.
  • train/validation split ratio = 90/10
    Section IV.A; chosen by hand; the headline numbers are computed on this validation split.
  • data augmentation scheme = 4x (color jitter, horizontal and vertical flips)
    Section IV.A; augmentation before the split creates the leakage risk that determines the credibility of the reported comparison.
  • learning rate schedule
    Section IV.A mentions 'a scheduler was adapted to adjust the learning rate dynamically' but never reports the schedule, base learning rate, or optimizer; these choices affect the comparison.
  • early stopping criterion
    Section IV.A uses early stopping evaluated on the validation set, so the reported validation metrics are a selected maximum, not an unbiased estimate.
  • CBAM reduction ratio
    The CBAM channel attention uses a reduction ratio for the MLP hidden size (Section III.C.1) but the value is not reported; the standard default is 16.
assumptions (4)
  • domain assumption The DSPS dataset's PCI labels, calculated according to ASTM standards, are accurate ground truth for training and evaluation.
    Section IV.A states labels were 'calculated according to ASTM standards, focusing on crack type, extent, and severity'; the entire evaluation treats these as correct.
  • domain assumption Data augmentation (color jitter and flips) preserves the validity of the original PCI labels.
    Section IV.A augments all images and trains regression against the unchanged labels; if a flip or jitter changed the true condition, the labels would be noisy.
  • domain assumption No augmented copy of a source image appears in both training and validation.
    Section IV.A describes augmentation before the 90/10 split and never states the split is grouped by source image; the reported validation metrics depend on this being true.
  • standard math Standard definitions: MAE, MAPE, RMSE (Eqs. 4-6), the residual block F(x)+x, and the CBAM formulas (Eqs. 2-3) are correct and applicable as stated.
    Sections III.A and III.C restate standard formulas from prior literature; the paper relies on them without proof. Note Eq. (3) is internally inconsistent with its own description.

how reviews work

0 comments
Cite this review

Pith. "Pith review of An Improved ResNet50 Model for Predicting Pavement Condition Index (PCI) Directly from Pavement Images." pith.science (2026). https://pith.science/paper/CIMLDMI3

@misc{pith2026250418490,
  author       = {Pith},
  title        = {Pith review of: An Improved ResNet50 Model for Predicting Pavement Condition Index (PCI) Directly from Pavement Images},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CIMLDMI3}},
  note         = {Machine review of arXiv:2504.18490}
}
read the original abstract

Accurately predicting the Pavement Condition Index (PCI), a measure of roadway conditions, from pavement images is crucial for infrastructure maintenance. This study proposes an enhanced version of the Residual Network (ResNet50) architecture, integrated with a Convolutional Block Attention Module (CBAM), to predict PCI directly from pavement images without additional annotations. By incorporating CBAM, the model autonomously prioritizes critical features within the images, improving prediction accuracy. Compared to the original baseline ResNet50 and DenseNet161 architectures, the enhanced ResNet50-CBAM model achieved a significantly lower mean absolute percentage error (MAPE) of 58.16%, compared to the baseline models that achieved 70.76% and 65.48% respectively. These results highlight the potential of using attention mechanisms to refine feature extraction, ultimately enabling more accurate and efficient assessments of pavement conditions. This study emphasizes the importance of targeted feature refinement in advancing automated pavement analysis through attention mechanisms.

Figures

Figures reproduced from arXiv: 2504.18490 by the authors.

Figure 1
Figure 1. A diagram of ResNet50 architecture The ResNet50 architecture shown in [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Archtecture for DenseNet161 model B. DenseNet161 The DenseNet161 model shown in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Overview of the Convolutional Block Attention Module (CBAM) [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Proposed architecture for ResNet50-CBAM model [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Feature map visualization of results [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Comparison of best predicted PCI values for ResNet, DenseNet161 & ResNet50+CBAM models [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Comparison of worst predicted PCI values for ResNet50, DenseNet161 and ResNet50+CBAM models [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Proposed model performance: (Left) line plot comparing actual and predicted PCI values and (Right) scatter plot showing the correlation between [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: Resnet50 model performance: (Left) line plot comparing actual and predicted PCI values and (Right) scatter plot showing the correlation between [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 10
Figure 10. Figure 10: DenseNet161 model performance: (Left) line plot comparing actual and predicted PCI values and (Right) scatter plot showing the correlation [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

46 extracted references · 33 canonical work pages

  1. [4]

    Image2PCI -- A Multitask Learning Framework for Estimating Pavement Condition Indices Directly from Images

    N. J. Owor, H. Du, A. Daud, A. Aboah, and Y . Adu-Gyamfi, “Image2pci–a multitask learning framework for estimating pave- ment condition indices directly from images,” arXiv preprint arXiv:2310.08538, 2023

    work page Pith review arXiv 2023

  2. [23]

    Implementation of deep neural networks for pavement condition index prediction,

    M. Sirhan, S. Bekhor, and A. Sidess, “Implementation of deep neural networks for pavement condition index prediction,” Journal of Transportation Engineering, Part B: Pavements , vol. 148, no. 1, p. 04021070, 2022. [Online]. Available: https://doi.org/10.1061/JPEODX. 0000333

    work page doi:10.1061/jpeodx 2022

  3. [1]

    Research trends in pavement management during the first years of the 21st century: A bibliometric analysis during the 2000–2013 period,

    H. P ´erez-Acebo, A. Linares-Unamunzaga, R. Abej ´on, and E. Roj ´ı, “Research trends in pavement management during the first years of the 21st century: A bibliometric analysis during the 2000–2013 period,” Applied Sciences , vol. 8, no. 7, p. 1041, 2018. [Online]. Available: https://doi.org/10.3390/app8071041

    work page doi:10.3390/app8071041

  4. [2]

    Machine learning techniques for pavement condition evaluation,

    N. Sholevar, A. Golroo, and S. R. Esfahani, “Machine learning techniques for pavement condition evaluation,” Automation in Construction, vol. 136, p. 104190, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0926580522000632

    work page 2022

  5. [3]

    Pavesam–segment anything for pavement distress,

    N. J. Owor, Y . Adu-Gyamfi, A. Aboah, and M. Amo-Boateng, “Pavesam–segment anything for pavement distress,”Road Materials and Pavement Design, pp. 1–25, 2024. 11 Fig. 9. Resnet50 model performance: (Left) line plot comparing actual and predicted PCI values and (Right) scatter plot showing the correlation between actual and predicted PCI values Fig. 10. De...

    work page 2024

  6. [6]

    A simplified pavement condition index regression model for pavement evaluation,

    A. A. Elhadidy, S. M. El-Badawy, and E. E. Elbeltagi, “A simplified pavement condition index regression model for pavement evaluation,” International Journal of Pavement Engineering , vol. 22, no. 5, pp. 643–652, 2019. [Online]. Available: https://doi.org/10.1080/10298436. 2019.1633579

    work page arXiv 2019

  7. [7]

    Study of pavement condition index (pci) relationship with international roughness index (iri) on flexible pavement,

    R. P. Hasibuan and M. S. Surbakti, “Study of pavement condition index (pci) relationship with international roughness index (iri) on flexible pavement,” MATEC Web of Conferences , vol. 258, p. 03019, 2019. [Online]. Available: https://doi.org/10.1051/matecconf/201925803019

    work page arXiv 2019

  8. [9]

    Deep Learning Approaches in Pavement Distress Identification: A Review

    S. Guan, H. Liu, H. R. Pourreza, and H. Mahyar, “Deep learning approaches in pavement distress identification: A review,” arXiv preprint arXiv:2308.00828 , 2023. [Online]. Available: https: //arxiv.org/abs/2308.00828

    work page Pith review arXiv 2023

Show all 46 references
  1. [10]

    Development of overall pavement condition index for urban road network,

    Y . U. Shah, S. S. Jain, D. Tiwari, and M. K. Jain, “Development of overall pavement condition index for urban road network,” Procedia - Social and Behavioral Sciences , vol. 104, pp. 332–341, 2013. [Online]. Available: https://doi.org/10.1016/j.sbspro.2013.11.126

    2013 doi

  2. [11]

    Deep machine learning approach to develop a new asphalt pavement condition index,

    H. Majidifard, Y . Adu-Gyamfi, and W. G. Buttlar, “Deep machine learning approach to develop a new asphalt pavement condition index,” Construction and Building Materials , vol. 247, p. 118513, 2020. [Online]. Available: https://doi.org/10.1016/j.conbuildmat.2020.118513

    2020

  3. [12]

    Data analytics in asset management: Cost-effective prediction of the pavement condition index,

    S. M. Piryonesi and T. E. El-Diraby, “Data analytics in asset management: Cost-effective prediction of the pavement condition index,” Journal of Infrastructure Systems , vol. 26, no. 1, p. 04019036, 2020. [Online]. Available: https://doi.org/10.1061/(ASCE)IS. 1943-555X.0000512 12

    2020 doi

  4. [13]

    Relationship between pavement roughness and distress parameters for indian highways,

    S. Chandra, R. S. Chalumuri, A. K. Bharti, and B. Kangadurai, “Relationship between pavement roughness and distress parameters for indian highways,” Journal of Transportation Engineering , vol. 139, no. 5, pp. 467–475, 2013. [Online]. Available: https://doi.org/10.1061/ (ASCE)...

    2013

  5. [14]

    Performance prediction of interstate flexible pavement across the midwestern united states: Random-parameter regression vs artificial neural network,

    M. S. Yamany, T. U. Saeed, and M. V olovski, “Performance prediction of interstate flexible pavement across the midwestern united states: Random-parameter regression vs artificial neural network,” in Transportation Research Board 98th Annual Meeting , Washington, D.C., January...

    2019

  6. [15]

    The 1st data science for pavements challenge,

    A. Behzadian, T. W. Muturi, T. Zhang, H. Kim, A. Mullins, Y . Lu, N. J. Owor, Y . Adu-Gyamfi, W. Buttlar, M. Hamed, et al. , “The 1st data science for pavements challenge,”arXiv preprint arXiv:2206.04874, 2022

    2022 arXiv

  7. [16]

    Pavecap: The first multimodal framework for comprehensive pavement condition assessment with dense captioning and pci estimation,

    B. A. Kyem, E. K. O. Denteh, J. K. Asamoah, and A. Aboah, “Pavecap: The first multimodal framework for comprehensive pavement condition assessment with dense captioning and pci estimation,” arXiv preprint arXiv:2408.04110, 2024

    2024 arXiv

  8. [17]

    Predicting pavement condition index based on the utilization of machine learning techniques: A case study,

    A. A. Ali, M. Abdalrhman, A. Hussein, N. I. M. Yusoff, and U. Heneash, “Predicting pavement condition index based on the utilization of machine learning techniques: A case study,” Journal of Road Engineering , vol. 3, no. 3, pp. 266–278, 2023. [Online]. Available: https://doi....

    2023 doi

  9. [18]

    Asphalt pavement damage detection through deep learning technique and cost-effective equipment: A case study in urban roads crossed by tramway lines,

    M. Guerrieri, G. Parla, M. Khanmohamadi, and L. Neduzha, “Asphalt pavement damage detection through deep learning technique and cost-effective equipment: A case study in urban roads crossed by tramway lines,” Infrastructures, vol. 9, no. 2, p. 34, 2024. [Online]. Available: ht...

    2024 doi

  10. [19]

    A newly developed hybrid method on pavement maintenance and rehabilitation optimization applying whale optimization algorithm and random forest regression,

    H. Naseri, H. Jahanbakhsh, A. Foomajd, N. Galustanian, M. M. Karimi, and E. O. D. Waygood, “A newly developed hybrid method on pavement maintenance and rehabilitation optimization applying whale optimization algorithm and random forest regression,” International Journal of Pav...

    2022

  11. [20]

    Deepsegmenter: Temporal action localization for detecting anomalies in untrimmed naturalistic driving videos,

    A. Aboah, U. Bagci, A. R. Mussah, N. J. Owor, and Y . Adu-Gyamfi, “Deepsegmenter: Temporal action localization for detecting anomalies in untrimmed naturalistic driving videos,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5358–5364

    2023

  12. [21]

    Prediction of pavement overall condition index based on wrapper feature-selection techniques using municipal pavement data,

    R. Adesunkanmi, A. Al-Hamdan, and I. Nlenanya, “Prediction of pavement overall condition index based on wrapper feature-selection techniques using municipal pavement data,” Transportation Research Record, vol. 2678, no. 6, pp. 208–221, 2024. [Online]. Available: https://doi.or...

    2024 doi

  13. [22]

    A cost effective solution for pavement crack inspection using cameras and deep neural networks,

    Q. Mei and M. G ¨ul, “A cost effective solution for pavement crack inspection using cameras and deep neural networks,” Construction and Building Materials , vol. 256, p. 119397, 2020. [Online]. Available: https://doi.org/10.1016/j.conbuildmat.2020.119397

    2020

  14. [24]

    Attention, please! a survey of neural attention models in deep learning,

    A. de Santana Correia and E. L. Colombini, “Attention, please! a survey of neural attention models in deep learning,” Artificial Intelligence Review, vol. 55, no. 8, pp. 6037–6124, 2022. [Online]. Available: https://doi.org/10.1007/s10462-022-10148-x

    2022 doi

  15. [25]

    Asenn: Attention-based selective embedding neural networks for road distress prediction,

    B. Philip, Z. Xu, H. AlJassmi, Q. Zhang, and L. Ali, “Asenn: Attention-based selective embedding neural networks for road distress prediction,” Journal of Big Data , vol. 10, no. 1, p. 164, 2023. [Online]. Available: https://doi.org/10.1186/s40537-023-00845-x

    2023 doi

  16. [26]

    Automatic pavement crack detection and classification using multiscale feature attention network,

    W. Song, G. Jia, D. Jia, and H. Zhu, “Automatic pavement crack detection and classification using multiscale feature attention network,” IEEE Access , vol. 7, pp. 171 001–171 012, 2019. [Online]. Available: https://doi.org/10.1109/ACCESS.2019.2956191

    2019

  17. [27]

    Vision-language model- based polyformer for recognizing visual questions with multiple answer groundings

    A. Aboah, Y . Jeon, M. Park, and S. Park, “Vision-language model- based polyformer for recognizing visual questions with multiple answer groundings.”

  18. [28]

    Smartphone-based pavement roughness estimation using deep learning with entity embedding,

    A. Aboah and Y . Adu-Gyamfi, “Smartphone-based pavement roughness estimation using deep learning with entity embedding,” Advances in Data Science and Adaptive Analysis , vol. 12, no. 03n04, p. 2050007, 2020

    2020

  19. [29]

    The road pavement condition index (pci) evaluation and maintenance: A case study of yemen,

    F. M. Karim, K. A. H. Rubasi, and A. A. Saleh, “The road pavement condition index (pci) evaluation and maintenance: A case study of yemen,” Organization, Technology and Management in Construction: An International Journal , vol. 8, no. 1, pp. 1446–1455, 2016. [Online]. Availab...

    2016 doi

  20. [30]

    An investigation into the factors that influence the use of transportation network company services using national household travel survey data,

    A. Aboah, “An investigation into the factors that influence the use of transportation network company services using national household travel survey data,” Master’s thesis, Tennessee Technological University, 2019

    2019

  21. [31]

    Real-time multi- class helmet violation detection using few-shot data sampling technique and yolov8,

    A. Aboah, B. Wang, U. Bagci, and Y . Adu-Gyamfi, “Real-time multi- class helmet violation detection using few-shot data sampling technique and yolov8,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 5350–5358

    2023

  22. [32]

    Weather- adaptive synthetic data generation for enhanced power line inspection using stargan,

    B. A. Kyem, J. K. Asamoah, Y . Huang, and A. Aboah, “Weather- adaptive synthetic data generation for enhanced power line inspection using stargan,” IEEE Access, 2024

    2024

  23. [33]

    Gazesam: Interactive image segmentation with eye gaze and segment anything model,

    B. Wang, A. Aboah, Z. Zhang, H. Pan, and U. Bagci, “Gazesam: Interactive image segmentation with eye gaze and segment anything model,” in Gaze Meets Machine Learning Workshop . PMLR, 2024, pp. 254–265

    2024

  24. [34]

    All you need is data: A multimodal approach in understanding driver behavior

    K. Kwakye, Y . Seong, S. Yi, and A. Aboah, “All you need is data: A multimodal approach in understanding driver behavior.” SAGE Publications Sage CA: Los Angeles, CA, 2024, p. 10711813241275942

    2024

  25. [35]

    Automation recognition of pavement surface distress based on support vector machine,

    N. Li, X. Hou, X. Yang, and Y . Dong, “Automation recognition of pavement surface distress based on support vector machine,” in Proceedings of the 2nd International Conference on Intelligent Networks and Intelligent Systems (ICINIS) . IEEE, 2009, pp. 346–349. [Online]. Availab...

    2009 doi

  26. [36]

    Divneds: Diverse naturalistic edge driving scene dataset for autonomous vehicle scene understanding,

    J. O. Duah, A. Aboah, and S. Osafo-Gyamfi, “Divneds: Diverse naturalistic edge driving scene dataset for autonomous vehicle scene understanding,” IEEE Access, 2024

    2024

  27. [37]

    Integrating travel behavior forecasting and generative mod- eling for predicting future urban mobility and spatial transformations,

    E. Denteh, A. Danyo, J. K. Asamoah, B. A. Kyem, T. Addai, and A. Aboah, “Integrating travel behavior forecasting and generative mod- eling for predicting future urban mobility and spatial transformations,” arXiv preprint arXiv:2503.21158 , 2025

    2025 arXiv

  28. [38]

    QCA V - a method based on machine learning using hand-crafted features for crack detection from asphalt pavement surface images,

    Y . Fujita, K. Shimada, M. Ichihara, and Y . Hamamoto, “QCA V - a method based on machine learning using hand-crafted features for crack detection from asphalt pavement surface images,” in Proceedings of SPIE , vol. 10338. SPIE, 2017. [Online]. Available: https://doi.org/10.11...

    2017 doi

  29. [39]

    Cracking classification using minimum rectangular cover–based support vector machine,

    S. Wang, S. Qiu, W. Wang, D. Xiao, and K. C. P. Wang, “Cracking classification using minimum rectangular cover–based support vector machine,” Journal of Computing in Civil Engineering , vol. 31, no. 5, p. 04017027, 2017. [Online]. Available: https: //doi.org/10.1061/(ASCE)CP.1...

    2017

  30. [40]

    Context-cracknet: A context-aware framework for precise segmentation of tiny cracks in pavement images,

    B. A. Kyem, J. K. Asamoah, and A. Aboah, “Context-cracknet: A context-aware framework for precise segmentation of tiny cracks in pavement images,” arXiv preprint arXiv:2501.14413 , 2025

    2025 arXiv

  31. [41]

    Saam-reflectnet: Sign-aware attention-based multitasking framework for integrated traffic sign detection and retroreflectivity estimation,

    J. K. Asamoah, B. Agyei Kyem, N.-D. Obeng-Amoako, and A. Aboah, “Saam-reflectnet: Sign-aware attention-based multitasking framework for integrated traffic sign detection and retroreflectivity estimation,” Nathan-David and Aboah, Armstrong, Saam-Reflectnet: Sign-Aware Attention...

  32. [42]

    A machine learning study of the dynamic modulus of asphalt concretes: An application of m5p model tree algorithm,

    A. Behnood and D. Daneshvar, “A machine learning study of the dynamic modulus of asphalt concretes: An application of m5p model tree algorithm,” Construction and Building Materials , vol. 262, p. 120544, 2020. [Online]. Available: https://doi.org/10.1016/j. conbuildmat.2020.120544

    2020

  33. [43]

    Hybrid method: Automatic crack detection of asphalt pavement images using learning-based and density- based techniques,

    M. Sabouri and M. Mohammadi, “Hybrid method: Automatic crack detection of asphalt pavement images using learning-based and density- based techniques,” International Journal of Pavement Research and Technology, vol. 16, no. 2, pp. 431–443, 2023. [Online]. Available: https://doi...

    2023 doi

  34. [44]

    Deep learning-based visual crack detection using google street view images,

    M. Maniat, C. V . Camp, and A. R. Kashani, “Deep learning-based visual crack detection using google street view images,” Neural Computing and Applications , vol. 33, no. 21, pp. 14 565–14 582, 2021. [Online]. Available: https://doi.org/10.1007/s00521-021-06098-0

    2021 doi

  35. [45]

    Yolov5s-m: A deep learning network model for road pavement damage detection from urban street-view imagery,

    M. Ren, X. Zhang, X. Chen, B. Zhou, and Z. Feng, “Yolov5s-m: A deep learning network model for road pavement damage detection from urban street-view imagery,” International Journal of Applied Earth Observation and Geoinformation , vol. 120, p. 103335, 2023. [Online]. Available...

    2023

  36. [46]

    Automated pavement distress detection and deterioration analysis using street view map,

    X. Lei, C. Liu, L. Li, and G. Wang, “Automated pavement distress detection and deterioration analysis using street view map,” IEEE Access , vol. 8, pp. 76 163–76 172, 2020. [Online]. Available: https://doi.org/10.1109/ACCESS.2020.2989028

    2020

  37. [47]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778. [Online]. Available: https://doi.org/10.1109/CVPR.2016.90 13

    2016 doi

  38. [48]

    Visualizing and understanding convolutional networks,

    M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in Computer Vision – ECCV 2014 , ser. Lecture Notes in Computer Science, D. J. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds., vol. 8689. Springer International Publishing, 2014, pp. 818...

    2014

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.