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REVIEW 4 major objections 6 minor 48 references

Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A YOLOv8s-seg model detects and segments facial wrinkles with 90.7% precision and 87.0% mAP50 on a 131-image validation set.

desk verdict Routine YOLOv8 wrinkle-segmentation report undermined by its own mAP50-95 of 10.2% and an unsupported 'strong localization' claim. read the letter →

arxiv 2505.11424 v1 pith:VYLP3CTR submitted 2025-05-16 cs.CV cs.AIcs.LG

Rana Poureskandar , Shiva Razzagzadeh This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords YOLOv8objectdetectionsegmentationwrinklefacialanalysisdeeplearningcomputervisiontransfer
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 claims that a lightweight YOLOv8s-seg model, fine-tuned from COCO pretrained weights for 50 epochs on a facial wrinkle dataset, can detect and segment wrinkles with high precision and recall. On a 131-image validation set, the reported metrics are Precision 90.7%, Recall 89.1%, mAP50 87.0%, mAP50-95 10.2%, Mask Precision 80.7%, and Mask Recall 89.1%. The model performs best on forehead wrinkles, lower on frown lines, and the authors note failures on subtle wrinkles, complex lighting, and overlapping features. The stated purpose is to assess whether YOLOv8-based automation is feasible for wrinkle identification in clinical and commercial settings.

What carries the argument

The central object is the YOLOv8s-seg architecture, a single-stage detector with a modified CSPDarknet53 backbone, a PAN-FPN neck, and a head that outputs bounding boxes, segmentation masks, and class probabilities at once. The training recipe uses COCO pretrained weights for transfer learning, 50 epochs with the Adam optimizer, 640x640 resized inputs, and data augmentation such as random flipping, cropping, rotation, and brightness adjustment. The evaluative machinery is the standard set of Precision, Recall, mAP50, mAP50-95, plus Mask Precision and Mask Recall.

What would settle it

Re-run the same training recipe on a larger, independently labeled facial-wrinkle dataset at a fixed confidence threshold, using a properly versioned train/validation/test split. If Precision, Recall, or mAP50 on that held-out set drop materially below the reported values, or if the 131-image validation set is found to overlap the training distribution in ways that inflate the metrics, the paper's generalization claim fails.

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Extended reading notes

Core claim

The central discovery claimed is that a YOLOv8s-seg model trained with the reported recipe achieves strong bounding-box detection of facial wrinkles but substantially weaker high-IoU localization. The evidence is the validation set results: Precision 90.7%, Recall 89.1%, mAP50 87.0%, mAP50-95 10.2%, Mask Precision 80.7%, Mask Recall 89.1%. The large drop from mAP50 to mAP50-95 is a direct corollary of the authors' own observation that localization precision at higher IoU thresholds is suboptimal.

Load-bearing premise

The reported performance rests on the assumption that the 131-image validation set is representative enough, and that the unspecified confidence threshold for Precision and Recall does not distort the headline numbers; if the validation set is small or skewed, or the threshold is unusual, the metrics may not reflect real-world wrinkle detection.

Editorial extensions

If this is right

  • Fine-tuning YOLOv8s-seg on a small wrinkle dataset yields usable bounding-box detection with precision above 90%, but not fine-grained segmentation at high IoU thresholds.
  • Per-class results suggest forehead wrinkles are the easiest class, while frown lines and general wrinkles need more data or class-specific tuning.
  • The low mAP50-95 (10.2%) implies the model is suitable for coarse wrinkle screening, not for precise clinical measurement.
  • The paper's finding that validation loss rises near the end of training indicates the training recipe may be close to an overfitting point; early stopping or heavier regularization could change results.

Reading between the lines

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

  • A reader should treat the headline Precision/Recall as threshold-dependent: the paper never states the confidence threshold, and the Precision-Confidence curve shows precision 1.00 at 0.865 confidence, so the operating point matters.
  • The 131-image validation set is small and the total dataset size is unstated; until a larger, multi-source test set is used, these numbers should be read as a feasibility demonstration, not a deployment guarantee.
  • The paper does not compare against other segmentation architectures, so it remains untested whether YOLOv8s-seg is better than, say, Mask R-CNN or transformer-based segmenters on this task.
  • A natural next experiment would be an ablation of the data-augmentation set and an early-stopping schedule, since the paper's own curves suggest diminishing returns and rising validation loss in late epochs.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper presents an experimental study in which a YOLOv8s-seg model is fine-tuned for 50 epochs on a Roboflow facial-wrinkle dataset and evaluated on a 131-image validation split. The reported results include Precision 90.7%, Recall 89.1%, mAP50 87.0%, mAP50-95 10.2%, Mask Precision 80.7%, and Mask Recall 89.1%, together with per-class results for forehead wrinkles, frown lines, and general wrinkles. The abstract characterizes the model as demonstrating strong localization and segmentation capabilities, while the paper's own Section 3.6 states that mAP at higher IoU thresholds was suboptimal. The title promises "Improving Object Detection Performance," but no baseline or comparison is presented.

Significance. If the reported metrics were internally consistent and reproduced on a larger, well-defined dataset, the study would offer a modest benchmark for YOLOv8-based wrinkle segmentation. As it stands, the primary claims are undermined by (i) an unresolved contradiction between the abstract's "strong localization" and the reported mAP50-95 of 10.2%, (ii) an impossible numerical relationship between the overall and per-class metrics in Section 3.5, and (iii) the absence of any baseline, ablation, or statistical uncertainty quantification. The work is a conventional application of an existing architecture and does not currently provide a reliable basis for its improvement claims.

major comments (4)
  1. [Abstract and Section 3.5] The abstract's statement that the model "demonstrated strong localization and segmentation capabilities" is directly contradicted by the reported mAP50-95 of 10.2% against an mAP50 of 87.0%. In standard YOLO evaluation, mAP50-95 averages average precision over IoU thresholds from 0.5 to 0.95; such a large gap indicates that predictions have poor overlap with ground truth at stricter IoU thresholds. The paper itself acknowledges in Section 3.6 that "mAP values at higher IoU thresholds were suboptimal" and that under-segmentation occurred. The central claim of strong localization is therefore not supported by the paper's own evidence and must be corrected.
  2. [Section 3.5] The overall and per-class metrics are numerically inconsistent. With exactly three classes (forehead, frown, general wrinkles), the overall mAP50 of 87.0% should equal the mean of the per-class mAP50 values 85.7%, 81.6%, and 83.7%, which is 83.7%, not 87.0%. Moreover, the overall Precision of 90.7% cannot be greater than each of the per-class precision values (85.0%, 80.5%, 88.6%) if these are the only classes and the overall precision is the pooled statistic; as a weighted average of the per-class precisions, it must lie between the minimum and maximum of those values. At least one set of the reported numbers is therefore erroneous, and the results section does not provide a plausible account of these discrepancies.
  3. [Title, Section 3.4, and Section 5] The title's claim of "Improving Object Detection Performance" is unsubstantiated because the study contains no baseline. No comparison is made with any other model, training configuration, or previous result on the same dataset. The paper evaluates a single fine-tuned YOLOv8s-seg configuration and reports its metrics; that describes a performance study, not an improvement over any existing method. Without a baseline or ablation, the improvement claim cannot be supported.
  4. [Sections 3.1, 3.4, and 3.5] The evaluation protocol is not fully specified. The validation set consists of 131 images from an unversioned Roboflow dataset whose total size is never given; no confidence interval or error bar is reported; and the confidence threshold at which Precision and Recall are computed is not stated. The paper mentions in Section 3.1 that 10% of the data is held out for testing, but no test-set results are reported anywhere. These omissions make the reported metrics irreproducible and prevent the reader from assessing whether the observed performance reflects the model's generalization ability or the particular choice of operating point.
minor comments (6)
  1. [Figures 1-4] Figure 1 and Figure 3 appear to be the same precision-confidence curve; Figures 2 and 4 are recall-confidence curves with different maximum recalls (0.81 and 0.72) but identical captions. Please clarify what each figure represents and why duplicates are included.
  2. [Section 3.4] The text contains a discussion of confusion matrices and metrics applied to "diabetes mellitus cases," which is unrelated to the wrinkle segmentation task; this passage appears to be carried over from another context and should be removed or rewritten.
  3. [Section 2] The Related Work section is entirely about vehicle detection and tracking and does not discuss facial wrinkle detection, segmentation, or YOLOv8-seg. A proper literature review relevant to the paper's topic is needed.
  4. [Section 3.4] The exact training configuration is missing: batch size is described as "appropriate for GPU memory capacity," and the learning rate schedule and augmentation parameters are not quantified. Please report these values for reproducibility.
  5. [Equation (3)] Equation (3) contains a typographical error ("Re TPcall") and should read "Recall = TP/(TP+FN)."
  6. [References [38]-[48]] References [38]-[48] are cited to support standard definitions of precision, recall, and the confusion matrix, but those references concern diabetes classification, suicide prediction, and other unrelated topics; standard machine-learning textbooks or canonical metric papers would be more appropriate.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the paper reports a held-out validation measurement rather than deriving predictions from fitted inputs or self-citations.

full rationale

The paper's central claim is an empirical evaluation: a YOLOv8s-seg model was trained for 50 epochs on a Roboflow facial wrinkle dataset and evaluated on a 131-image validation set, with metrics reported in Section 3.5. There is no derivation chain in which an input quantity is defined in terms of an output quantity, no fitted parameter is renamed as a prediction, and no load-bearing argument rests on self-citations or an imported uniqueness theorem. The training and validation sets are separate splits of the same dataset, which is a standard held-out evaluation protocol rather than a circular construction; the metrics are measurements, not consequences of the model's assumptions. Concerns about the small, unversioned dataset, the unspecified confidence threshold, and the internal inconsistency between the abstract's 'strong localization' claim and the mAP50-95 of 10.2% are validity, reproducibility, and correctness issues, not circularity. The paper even acknowledges its own limitation in Section 3.6, stating that 'mAP values at higher IoU thresholds were suboptimal' and that the dataset size 'might restrict the model's ability to generalize.' Because no specific reduction of a claimed result to its own inputs can be quoted, the appropriate circularity score is 0.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

The paper's central claim rests entirely on a standard YOLOv8s-seg training run with multiple unspecified hyperparameters, an unversioned public dataset, and a validation-only evaluation. No new entity or first-principles parameter is introduced; the key unknowns are the exact training configuration and the representativeness of the 131-image validation set.

free parameters (5)
  • Batch size = unspecified
    Section 3.4 says 'a batch size appropriate for GPU memory capacity was selected' but no value is given; the trained model, and thus the reported metrics, depends on this choice.
  • Learning rate and scheduler = unspecified
    Section 3.4 mentions 'adaptive learning rate scheduler and the Adam optimizer' but no initial learning rate or schedule parameters are reported.
  • Confidence threshold = unspecified
    Precision and Recall are threshold-dependent, but the paper does not state the confidence threshold at which the 90.7% precision and 89.1% recall were computed.
  • Data augmentation parameters = unspecified
    Section 3.2 mentions random flipping, cropping, rotation, and brightness adjustments but gives no ranges or probabilities; these affect the trained model.
  • Dataset split ratios = 70/20/10
    The split is a design choice; the validation set size of 131 images depends on the total dataset size, which is not reported.
assumptions (3)
  • domain assumption The Roboflow facial wrinkle dataset annotations are accurate and representative
    Section 3.1 says the dataset is 'specifically tailored for facial wrinkle detection and segmentation', but no annotation protocol, quality control, or inter-annotator agreement is described.
  • domain assumption The 131-image validation set is representative of the broader wrinkle image population
    Section 3.5 reports metrics only on the validation set; generalization to unseen conditions is asserted despite the small size and the paper's own limitation note about 'relatively small' dataset (Section 3.6).
  • domain assumption YOLOv8s-seg with COCO-pretrained weights is a suitable architecture for wrinkle segmentation
    Section 3.3 selects this variant without any comparison to other architectures or prior wrinkle segmentation models.

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Cite this review

Pith. "Pith review of Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study." pith.science (2026). https://pith.science/paper/VYLP3CTR

@misc{pith2026250511424,
  author       = {Pith},
  title        = {Pith review of: Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VYLP3CTR}},
  note         = {Machine review of arXiv:2505.11424}
}
read the original abstract

This study evaluated the performance of a YOLOv8-based segmentation model for detecting and segmenting wrinkles in facial images.

Figures

Figures reproduced from arXiv: 2505.11424 by the authors.

Figure 1
Figure 1. Precision-Confidence curve visualizes the relationship between the model's confidence in its predictions and its precision The Precision-Confidence curve visualizes the relationship between the model's confidence in its predictions and its precision. Each line represents a different class: forehead, frown, and wrinkle. The x-axis indicates the model's confidence level, ranging from 0 to 1, with 1 representing the hi… view at source ↗
Figure 2
Figure 2. Recall-Confidence curve visualizes the relationship between the model's confidence in its predictions and its recall The Recall-Confidence curve visualizes the relationship between the model's confidence in its predictions and its recall. Each line represents a different class: forehead, frown, and wrinkle. The x-axis indicates the model's confidence level, ranging from 0 to 1, with 1 representing the highest confid… view at source ↗
Figure 3
Figure 3. Precision-Confidence curve visualizes the relationship between the model's confidence in its predictions and its precision. The Precision-Confidence curve visualizes the relationship between the model's confidence in its predictions and its precision. Each line represents a different class: forehead, frown, and wrinkle. The x-axis indicates the model's confidence level, ranging from 0 to 1, with 1 representing the h… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Recall-Confidence curve visualizes the relationship between the model's confidence in its predictions and its recall. The Recall-Confidence curve visualizes the relationship between the model's confidence in its predictions and its recall. Each line represents a differ…
Figure 5
Figure 5. Figure 5: provided graph illustrates the training and validation loss of a deep learning model over 50 epochs. [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

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Reference graph

Works this paper leans on

48 extracted references · 46 canonical work pages

  1. [1]

    Moving object detection based on enhanced YOLO -V2 model

    M. Safaldin, N. Zaghden and M. Mejdoub, "Moving object detection based on enhanced YOLO -V2 model", Proc. 5th Int. Congr. Human-Computer Interact. Optim. Robotic Appl. (HORA), pp. 1-8, Jun. 2023

    work page 2023

  2. [2]

    Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance

    S. Ammar, T. Bouwmans, N. Zaghden and M. Neji, "Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance", IET Image Process., vol. 14, no. 8, pp. 1490-1501, Jun. 2020

    work page 2020

  3. [3]

    From moving objects detection to classification and recognition: A review for smart environments

    S. Ammar, T. Bouwmans, N. Zaghden and N. Mahmoud, "From moving objects detection to classification and recognition: A review for smart environments", Proc. Towards Smart World, pp. 289-316, 2020

    work page 2020

  4. [4]

    Semantic analysis of moving objects in video sequences

    E. M. Ibrahim, M. Mejdoub and N. Zaghden, "Semantic analysis of moving objects in video sequences", Proc. Int. Conf. Emerg. Technol. Intell. Syst., pp. 257-269, 2022

    work page 2022

  5. [5]

    An overview of GAN -DeepFakes detection: Proposal improvement and evaluation

    F. Ben Aissa, M. Hamdi, M. Zaied and M. Mejdoub, "An overview of GAN -DeepFakes detection: Proposal improvement and evaluation", Multimedia Tools Appl., vol. 83, no. 11, pp. 32343-32365, Sep. 2023

    work page 2023

  6. [6]

    Fer -YOLO: Detection and classification based on facial expressions

    H. Ma, T. Celik and H. Li, "Fer -YOLO: Detection and classification based on facial expressions", Proc. Image Graphics: 11th Int. Conf., pp. 28-39, 2021

    work page 2021

  7. [7]

    A review and comparative study on probabilistic object detection in autonomous driving

    D. Feng, A. Harakeh, S. L. Waslander and K. Dietmayer, "A review and comparative study on probabilistic object detection in autonomous driving", IEEE Trans. Intell. Transp. Syst., vol. 23, no. 8, pp. 9961-9980, Aug. 2022

    work page 2022

  8. [8]

    Recent advances in small object detection based on deep learning: A review

    K. Tong, Y. Wu and F. Zhou, "Recent advances in small object detection based on deep learning: A review", Image Vis. Comput., vol. 97, May 2020

    work page 2020

Show all 48 references
  1. [9]

    Exudate regeneration for automated exudate detection in retinal fundus images

    M. Hussain, H. Al-Aqrabi, M. Munawar, R. Hill and S. Parkinson, "Exudate regeneration for automated exudate detection in retinal fundus images", IEEE Access, vol. 11, pp. 83934-83945, 2022

    2022

  2. [10]

    Deployment of AI -based RBF network for photovoltaics fault detection procedure

    M. Hussain, M. Dhimish, V. Holmes and P. Mather, "Deployment of AI -based RBF network for photovoltaics fault detection procedure", AIMS Electron. Electr. Eng., vol. 4, no. 1, pp. 1-18, 2020

    2020

  3. [11]

    Automated surface defect detection framework using machine vision and convolutional neural networks

    S. A. Singh and K. A. Desai, "Automated surface defect detection framework using machine vision and convolutional neural networks", J. Intell. Manuf., vol. 34, no. 4, pp. 1995-2011, Apr. 2023

    1995

  4. [12]

    A review of machine learning for the optimization of production processes

    D. Weichert, P. Link, A. Stoll, S. Rüping, S. Ihlenfeldt and S. Wrobel, "A review of machine learning for the optimization of production processes", Int. J. Adv. Manuf. Technol., vol. 104, no. 5, pp. 1889-1902, Oct. 2019

    1902

  5. [13]

    Deep learning for smart manufacturing: Methods and applications

    J. Wang, Y. Ma, L. Zhang, R. X. Gao and D. Wu, "Deep learning for smart manufacturing: Methods and applications", J. Manuf. Syst., vol. 48, pp. 144-156, Jul. 2018

    2018

  6. [14]

    Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection

    D. Weimer, B. Scholz-Reiter and M. Shpitalni, "Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection", CIRP Ann., vol. 65, no. 1, pp. 417-420, 2016

    2016

  7. [15]

    Experiments with neural net object detection system YOLO on small training datasets for intelligent robotics

    S. Kulik and A. Shtanko, "Experiments with neural net object detection system YOLO on small training datasets for intelligent robotics", Proc. Adv. Technol. Robot. Intell. Syst. ITR, pp. 157-162, 2020

    2020

  8. [16]

    Using deep learning to detect defects in manufacturing: A comprehensive survey and current challenges

    J. Yang, S. Li, Z. Wang, H. Dong, J. Wang and S. Tang, "Using deep learning to detect defects in manufacturing: A comprehensive survey and current challenges", Materials, vol. 13, no. 24, pp. 5755, Dec. 2020

    2020

  9. [17]

    Optimizing the trade -off between single -stage and two -stage deep object detectors using image difficulty prediction

    P. Soviany and R. T. Ionescu, "Optimizing the trade -off between single -stage and two -stage deep object detectors using image difficulty prediction", Proc. 20th Int. Symp. Symbolic Numeric Algorithms for Scientific Comput. (SYNASC), pp. 209-214, Sep. 2018

    2018

  10. [18]

    Overview of two -stage object detection algorithms

    L. Du, R. Zhang and X. Wang, "Overview of two -stage object detection algorithms", J. Phys. Conf. Ser., vol. 1544, no. 1, May 2020

    2020

  11. [19]

    A review of object detection models based on convolutional neural network

    F. Sultana, A. Sufian and P. Dutta, "A review of object detection models based on convolutional neural network" in Intelligent Computing: Image Processing Based Applications, Kolkata, India:Springer, pp. 1-16, 2020

    2020

  12. [20]

    YOLO: A Brief History; 2023

    Jocher, G.; Munawar, M.R.; Chaurasia, A. YOLO: A Brief History; 2023. Available online: https://www.scirp.org/reference/referencespapers?referenceid=3532980 (accessed on 11 February 2023)

    2023

  13. [21]

    YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain

    Al Rabbani Alif, M.; Hussain, M. YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain. arXiv 2024, arXiv:2406.10139

    2024 arXiv

  14. [22]

    Real-time visual detection and tracking system for traffic monitoring

    Fernandez-Sanjurjo, M.; Bosquet, B.; Mucientes, M.; Brea, V.M. Real-time visual detection and tracking system for traffic monitoring. Eng. Appl. Artif. Intell. 2019, 85, 410–420

    2019

  15. [23]

    A background subtraction algorithm for detecting and tracking vehicles

    Mandellos, N.A.; Keramitsoglou, I.; Kiranoudis, C.T. A background subtraction algorithm for detecting and tracking vehicles. Expert Syst. Appl. 2011, 38, 1619–1631

    2011

  16. [24]

    Moving vehicle detection by optimal segmentation of the dynamic stixel world

    Erbs, F.; Barth, A.; Franke, U. Moving vehicle detection by optimal segmentation of the dynamic stixel world. In Proceedings of the 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany, 5–9 June 2011; pp. 951–956

    2011

  17. [25]

    Faster R -CNN: Towards real -time object detection with region proposal networks

    Ren, S.; He, K.; Girshick, R.; Sun, J. Faster R -CNN: Towards real -time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 2016, 39, 1137–1149

    2016

  18. [26]

    Ssd: Single shot multibox detector

    Liu, W.; Anguelov, D.; Erhan, D.; Szegedy, C.; Reed, S.; Fu, C.Y.; Berg, A.C. Ssd: Single shot multibox detector. In Proceedings of the Computer Vision –ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, 11 –14 October 2016; Proceedings, Part I 14. Springer: Berl...

    2016

  19. [27]

    Deep residual learning for image recognition

    He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 26 June–1 July 2016; pp. 770–778

    2016

  20. [28]

    BoltVision: A Comparative Analysis of CNN, CCT, and ViT in Achieving High Accuracy for Missing Bolt Classification in Train Components

    Alif, M.A.R.; Hussain, M.; Tucker, G.; Iwnicki, S. BoltVision: A Comparative Analysis of CNN, CCT, and ViT in Achieving High Accuracy for Missing Bolt Classification in Train Components. Machines 2024, 12, 93

    2024

  21. [29]

    Lightweight Convolutional Network with Integrated Attention Mechanism for Missing Bolt Detection in Railways

    Alif, M.A.R.; Hussain, M. Lightweight Convolutional Network with Integrated Attention Mechanism for Missing Bolt Detection in Railways. Metrology 2024, 4, 254–278

    2024

  22. [30]

    Attention-Based Automated Pallet Racking Damage Detection

    Alif, M.A.R. Attention-Based Automated Pallet Racking Damage Detection. Int. J. Innov. Sci. Res. Technol. 2024, 9, 728– 740

    2024

  23. [31]

    YOLO -v5 Variant Selection Algorithm Coupled with Representative Augmentations for Modelling Production-Based Variance in Automated Lightweight Pallet Racking Inspection

    Hussain, M. YOLO -v5 Variant Selection Algorithm Coupled with Representative Augmentations for Modelling Production-Based Variance in Automated Lightweight Pallet Racking Inspection. Big Data Cogn. Comput. 2023, 7, 120

    2023

  24. [32]

    Lightweight convolutional network for automated photovoltaic defect detection

    Zahid, A.; Hussain, M.; Hill, R.; Al -Aqrabi, H. Lightweight convolutional network for automated photovoltaic defect detection. In Proceedings of the 2023 9th International Conference on Information Technology Trends (ITT), Dubai, United Arab Emirates, 24–25 May 2023; IEEE: Ne...

    2023

  25. [33]

    State-of-the-Art Bangla Handwritten Character Recognition Using a Modified Resnet-34 Architecture

    Alif, M.A.R. State-of-the-Art Bangla Handwritten Character Recognition Using a Modified Resnet-34 Architecture. Int. J. Innov. Sci. Res. Technol. 2024, 9, 438–448

    2024

  26. [34]

    An improved YOLOv2 for vehicle detection

    Sang, J.; Wu, Z.; Guo, P.; Hu, H.; Xiang, H.; Zhang, Q.; Cai, B. An improved YOLOv2 for vehicle detection. Sensors 2018, 18, 4272. [Google Scholar] [CrossRef]

    2018

  27. [35]

    The real -time detection of traffic participants using YOLO algorithm

    Ćorović, A.; Ilić, V.; Ðurić, S.; Marijan, M.; Pavković, B. The real -time detection of traffic participants using YOLO algorithm. In Proceedings of the 2018 26th Telecommunications Forum (TELFOR), Belgrade, Serbia, 20 –21 November 2018; pp. 1–4

    2018

  28. [36]

    A video streaming vehicle detection algorithm based on YOLOv4

    Hu, X.; Wei, Z.; Zhou, W. A video streaming vehicle detection algorithm based on YOLOv4. In Proceedings of the 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, 12–14 March 2021; Volume 5, pp. 2081–2086

    2021

  29. [37]

    Detecting heavy goods vehicles in rest areas in winter conditions using YOLOv5

    Kasper-Eulaers, M.; Hahn, N.; Berger, S.; Sebulonsen, T.; Myrland, Ø.; Kummervold, P.E. Detecting heavy goods vehicles in rest areas in winter conditions using YOLOv5. Algorithms 2021, 14, 114

    2021

  30. [38]

    Abdollahi, J., & Aref, S. (2024). Early Prediction of Diabetes Using Feature Selection and Machine Learning Algorithms. SN Computer Science, 5(2), 217

    2024

  31. [39]

    A., Negaresh, M., Abdollahi, J., Mohammadi, M., Ghobadi, H., Mohammadzadeh, B., & Amani, F

    Javadzadeh Barzaki, M. A., Negaresh, M., Abdollahi, J., Mohammadi, M., Ghobadi, H., Mohammadzadeh, B., & Amani, F. (2022, July). USING DEEP LEARNING NETWORKS FOR CLASSIFICATION OF LUNG CANCER NODULES IN CT IMAGES. In Iranian Congress of Radiology (Vol. 37, No. 2, pp. 34-34). I...

    2022

  32. [40]

    (2024, February)

    Abdollahi, J., & Mehrpour, O. (2024, February). Using Machine Learning Algorithms for Coronary Artery Disease (CAD) Prediction Prediction of Coronary Artery Disease (CAD) Using Machine Learning Algorithms. In 2024 10th International Conference on Artificial Intelligence and Ro...

    2024

  33. [41]

    Barzaki, M. A. J. Z., Abdollahi, J., Negaresh, M., Salimi, M., Zolfaghari, H., Mohammadi, M., ... & Amani, F. (2023, November). Using Deep Learning for Classification of Lung Cancer on CT Images in Ardabil Province: Classification of Lung Cancer using Xcep tion. In 2023 13th I...

    2023

  34. [42]

    (2024, February)

    Abdollahi, J., & Amani, F. (2024, February). The impact of analysis Suicide using machine learning algorithms in Ardabil: A performance analysis: using machine learning algorithms in analysis Suicide. In 2024 10th International Conference on Artificial Intelligence and Robotic...

    2024

  35. [43]

    (2024, February)

    Amani, F., Abdollahi, J., & Amani, P. (2024, February). Identify the Factors Influencing Suicide among Ardabil city People Using Feature Selection: Identify the Factors Influencing Suicide among Ardabil using machine learning. In 2024 10th International Conference on Artificia...

    2024

  36. [44]

    I., & Naji, H

    Nouri-Moghaddam, B., Shahabian, M. I., & Naji, H. R. Multi-Agent Based PGP Architecture. architecture, 3, 5

  37. [45]

    Nouri-Moghaddam, B., & Naji, H. R. (2013, May). Improving HBQ Authentication and Access control in wireless sensor network. In The 5th Conference on Information and Knowledge Technology (pp. 82-87). IEEE

    2013

  38. [46]

    Tarif, M., Mirzaei, A., & Nouri -Moghaddam, B. (2024). Optimizing RPL Routing Using Tabu Search to Improve Link Stability and Energy Consumption in IoT Networks. arXiv preprint arXiv:2408.06702

    2024 arXiv

  39. [47]

    Nouri-Moghaddam, B., & Naji, H. R. (2015). A novel authentication and access control framework in wireless sensor networks. Journal of Advanced Computer Science and Technology, 4(1), 122-135

    2015

  40. [48]

    Nouri-Moghaddam, B., Ghazanfari, M., & Fathian, M. (2020). A novel filter -wrapper hybrid gene selection approach for microarray data based on multi-objective forest optimization algorithm. Decision Science Letters, 9(3), 271-290

    2020

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.