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REVIEW 2 major objections 6 minor 51 references

Smart Parking with Pixel-Wise ROI Selection for Vehicle Detection Using YOLOv8, YOLOv9, YOLOv10, and YOLOv11

T0 review · 2 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A pixel-wise post-processing mask lets off-the-shelf YOLO models count parked cars with 99.68% balanced accuracy.

desk verdict The 99.68% balanced accuracy claim is not computable from the count-only labels the paper describes; the useful core is the post-processing mask trick and the hardware latency comparison. read the letter →

arxiv 2412.01983 v2 pith:VS3YCHXF submitted 2024-12-02 cs.CV cs.LG

Gustavo P. C. P. da Luz , Gabriel Massuyoshi Sato , Luis Fernando Gomez Gonzalez , Juliana Freitag Borin This is my paper · ORCID
classification cs.CVcs.LG
keywords smartparkingYOLOv8YOLOv9YOLOv10YOLOv11regionofinterestvehicledetectionedgecomputing
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 establish that a cheap fixed camera plus off-the-shelf YOLO models can count parked cars almost perfectly if the region of interest is selected after detection, pixel by pixel, rather than by masking the image before detection. The proposed post-processing mask, applied to the centers of detected bounding boxes, beats the pre-processing mask for all eight YOLO variants tested, and with YOLOv9e it reaches 99.68% balanced accuracy and an F1-score of 0.9975 on 3,484 images of a 16-space parking lot. The paper also shows that inference can run at the edge, from 1 to 92 seconds per image on Raspberry Pi devices, while sending only the final count to the cloud, and estimates that a camera system becomes cheaper than per-space sensors beyond roughly four spaces. A reader should care because, if correct, this is a simple, low-cost path to privacy-preserving parking occupancy monitoring without fine-tuning or specialized hardware.

What carries the argument

The central mechanism is the pixel-wise post-processing mask: a binary mask, drawn once in an image editor, that labels each pixel as inside or outside the parking ROI. After YOLO inference, the mask is not applied to the image; the detector's bounding boxes are filtered by looking up the pixel value at each box's center and keeping the box only if that pixel is black. This preserves full image context for the network while restricting the count to the area of interest, and it is the component that produces the reported accuracy gap.

What would settle it

Inspect the dataset's annotation files: if each image has only an integer vehicle count and no per-space occupied or empty ground truth, then the per-space TP/TN/FP/FN values and the 99.68% balanced accuracy cannot be reproduced from the described data, and recomputing at the image-count level would yield different error rates.

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Extended reading notes

Core claim

The paper's central claim is that applying a pixel-wise region-of-interest mask after object detection, rather than before, materially improves vehicle counting in parking-lot images. The same hand-drawn mask is used either to gray out non-parking areas in the input image or to filter detected bounding boxes by checking whether each box's center falls inside the black ROI. Across all eight YOLO variants tested, the post-processing approach wins; with YOLOv9e it reaches 99.68% balanced accuracy and an F1-score of 0.9975 on a 3,484-image dataset of a 16-space lot, without any fine-tuning of the pretrained models.

Load-bearing premise

The load-bearing premise is that the dataset contains per-space occupancy labels rather than only a total vehicle count per image, since the reported confusion matrices and balanced accuracy are per-space metrics; the paper's labeling description mentions only counting vehicles per image.

Editorial extensions

If this is right

  • Post-processing ROI selection improves balanced accuracy over pre-processing for every YOLO model tested, with gains exceeding 28 percentage points for the smallest model.
  • A deployment needs no training: pretrained COCO weights, one hand-drawn mask, and a fixed camera are enough for near-perfect occupancy counts in a small lot.
  • The method can run at the edge on a Raspberry Pi, sending only the number of vehicles to the cloud and keeping images on site.
  • The camera solution is estimated to become cheaper than per-space sensors once a parking lot has more than about four spaces.
  • The lightest models run in about one to two seconds per image on Raspberry Pi hardware with only a modest accuracy penalty, making always-on occupancy monitoring feasible on very cheap devices.

Reading between the lines

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

  • The center-point-in-mask filter is object-class agnostic, so the same post-processing trick should transfer to other fixed-camera counting tasks such as people in queues or items on shelves; the paper does not test those cases.
  • The 99.68% figure is tied to one camera angle and a 16-space layout; multi-camera lots would need per-camera masks and a fusion step, which the paper flags only as future work.
  • Mask geometry is a free parameter that the paper does not sweep; redrawing the same ROI slightly larger or smaller would show how much of the reported accuracy depends on hand-drawn boundaries.
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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

2 major / 6 minor

Summary. The paper proposes a smart-parking system that uses off-the-shelf pretrained YOLOv8, YOLOv9, YOLOv10, and YOLOv11 models to count vehicles in a fixed 16-space parking lot. A hand-drawn pixel-wise ROI mask is applied either as a pre-processing step (graying out pixels outside the ROI) or as a post-processing step (keeping only detections whose bounding-box center falls inside the ROI). The paper reports per-space accuracy, balanced accuracy, and F1-score for 16 model/mask configurations on a custom 3,484-image dataset, with a headline balanced accuracy of 99.68% for YOLOv9e with post-processing. It also reports inference-time measurements on six hardware platforms and a cost comparison between camera-based and sensor-based solutions.

Significance. If the reported evaluation were valid, the paper would provide a useful engineering data point for fixed-camera parking occupancy counting with recent YOLO versions. The post-processing ROI filter is simple and practical, and the latency benchmark across GPUs, CPUs, and Raspberry Pi devices together with the cost analysis are concrete, reproducible contributions. The paper correctly gives credit to the use of off-the-shelf models and does not claim to train a new detector. However, the central quantitative claim depends on per-space confusion-matrix metrics that cannot be computed from the count-only labels described in the manuscript, so the headline accuracy and the model-comparison table are not supported by the described evaluation protocol.

major comments (2)
  1. [§4.1, §4.4, §6] The evaluation protocol is internally inconsistent. Section 4.1 describes labeling only as "counting the number of vehicles in each image," with no spatial annotation, while Section 4.4 defines TP, TN, FP, and FN at the level of individual parking spaces (e.g., TP = "correctly predicted empty space"). Section 6 explicitly lists "improving the labeling of the dataset by following bounding box coordinates" as future work, confirming that no per-space or bounding-box ground truth currently exists. Under count-only labels, the per-space confusion matrices in Figures 7–8 and all metrics in Table 3 are not uniquely determined. For example, for one image with 16 spaces, a true count of 5 vehicles and a predicted count of 3 vehicles, the number of correctly predicted empty spaces can range from 8 to 11, yielding per-image balanced accuracies between 30.77% and 92.31% depending on which specific spaces are misclassified. The reported 99.68% balanced accuracy for YOLOv9e is therefore not computable from the described dataset and labeling procedure, and this is a load-bearing flaw because that value is the paper's headline result.
  2. [§4.2, §5.1, Table 3] The headline 99.68% balanced accuracy is the maximum over 16 model/mask configurations evaluated on the same 3,484 images, with no confidence intervals, repeated runs, or a separate selection set. Section 4.2 states that "100% of the data was used as validation," so the reported best value is a selected maximum rather than a statistically grounded estimate. The qualitative conclusion that post-processing beats pre-processing is robust across all eight model rows in Table 3, but the specific numerical claim needs an uncertainty estimate or a held-out evaluation protocol to support it.
minor comments (6)
  1. [§4.4] The definitions of TP and TN are reversed relative to the standard convention: TP is defined as "correctly predicted empty space" and TN as "correctly predicted vehicle." This is confusing and should be clarified by explicitly naming the positive class, or by using labels such as "empty-space detection" rather than the standard TP/TN terminology.
  2. [§4.1] The statement that filtering out empty images "helps to balance the number of backgrounds and vehicles" is unclear; removing all images with zero vehicles removes the empty-parking-lot class entirely, which seems to unbalance rather than balance the dataset.
  3. [§5.2, Table 4] Please specify whether the same 80 random images and the same random seed were used for all hardware/model timing measurements, and state how many repeated runs produced the reported standard deviations.
  4. [§4.3] The mask was manually drawn in GIMP on one image; the paper should describe how the 16 parking spaces are represented in the mask and whether the mask requires fixed camera alignment or per-image registration.
  5. [§6] The future-work sentence about "improving the labeling of the dataset by following bounding box coordinates" is in direct tension with the space-level confusion-matrix metrics used in Section 4.4; this inconsistency should be resolved explicitly.
  6. [References] Reference [32] contains a typo ("supremum to" should be "superior to"), and reference [51] lacks journal, volume, and page information.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the accuracy claim rests on off-the-shelf YOLO weights, a hand-drawn mask, and independent human count labels; no fitted parameter or self-citation chain forces the result.

full rationale

The paper's central claim (99.68% balanced accuracy for YOLOv9e with the post-processing mask, Table 3) is an empirical comparison. The models are used off-the-shelf: 'The dataset was not used to train the model, as we used only the pre-trained weights of the network, so 100% of the data was used as validation.' The ROI mask is hand-drawn, not learned: 'the same mask was created by selecting a random picture and manually editing it using the free image editor software GNU Image Manipulation Program (GIMP)'. The labels are independent human counts: 'We labeled the dataset, counting the number of vehicles in each image, including cars and trucks.' No fitted parameter is renamed as a prediction; the post-processing method simply filters detections by the mask and compares the resulting count with the labels. The same-author citations ([33], [50]) are used only to support prior feasibility of edge inference on constrained devices, not to justify the accuracy claim, so they are not load-bearing. The admission that 'similar methods can be found in the Ultralytics library... limited to regular polygonal shapes' is an incremental novelty statement, not a circular renaming. The most serious defect is the mismatch between count-only labels in Section 4.1 and the per-space confusion-matrix metrics in Section 4.4 (TP defined as 'Correctly predicted empty space', TN as 'Correctly predicted vehicle'), especially since Section 6 lists 'improving the labeling of the dataset by following bounding box coordinates' as future work. That is a data-availability/internal-consistency problem that may make the reported balanced accuracy non-computable from the described protocol, but it is not a circular derivation: the metric is not defined in terms of the method's output or of the claim itself. Similarly, reporting the best of 16 configurations on the same evaluation set is a selection-maximum effect, not a fitted-input-called-prediction circularity. Therefore no circular step can be exhibited from the paper's own equations or citations, and the appropriate score is 0.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

No new physical entities or mediators are introduced. The paper's central inputs are the hand-made ROI mask and pretrained YOLO weights, both drawn from outside the paper. The main unexamined axioms concern label granularity and metric validity.

free parameters (1)
  • Hand-drawn ROI mask = binary mask (black=ROI, white=excluded), created in GIMP
    The mask is manually drawn on one reference image and applied to all images; it determines which detections count and is not learned from data. It is a hand-chosen input to the pipeline.
assumptions (4)
  • domain assumption COCO-pretrained YOLO weights generalize to this parking lot without fine-tuning
    Section 4.2 states the dataset was not used to train, relying entirely on pretrained weights for vehicle detection.
  • domain assumption Image-level vehicle counts are accurate and support per-space confusion-matrix metrics
    Section 4.1 describes labeling as counting vehicles per image, while Section 4.4 defines TP/TN/FP/FN per parking space; this assumption is questionable and is the weakest premise.
  • domain assumption The center point of a bounding box correctly determines whether a detected vehicle is inside the ROI
    Algorithm 2 keeps a detection only if its center pixel in the mask is black; partial vehicles with centers outside the mask are discarded.
  • domain assumption Removing images with no vehicles does not bias the comparison of ROI methods
    Section 4.1 filters out 22% of images to balance classes; the effect on measured balanced accuracy is not analyzed.

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Cite this review

Pith. "Pith review of Smart Parking with Pixel-Wise ROI Selection for Vehicle Detection Using YOLOv8, YOLOv9, YOLOv10, and YOLOv11." pith.science (2026). https://pith.science/paper/VS3YCHXF

@misc{pith2026241201983,
  author       = {Pith},
  title        = {Pith review of: Smart Parking with Pixel-Wise ROI Selection for Vehicle Detection Using YOLOv8, YOLOv9, YOLOv10, and YOLOv11},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VS3YCHXF}},
  note         = {Machine review of arXiv:2412.01983}
}
read the original abstract

The increasing urbanization and the growing number of vehicles in cities have underscored the need for efficient parking management systems. Traditional smart parking solutions often rely on sensors or cameras for occupancy detection, each with its limitations. Recent advancements in deep learning have introduced new YOLO models (YOLOv8, YOLOv9, YOLOv10, and YOLOv11), but these models have not been extensively evaluated in the context of smart parking systems, particularly when combined with Region of Interest (ROI) selection for object detection. Existing methods still rely on fixed polygonal ROI selections or simple pixel-based modifications, which limit flexibility and precision. This work introduces a novel approach that integrates Internet of Things, Edge Computing, and Deep Learning concepts, by using the latest YOLO models for vehicle detection. By exploring both edge and cloud computing, it was found that inference times on edge devices ranged from 1 to 92 seconds, depending on the hardware and model version. Additionally, a new pixel-wise post-processing ROI selection method is proposed for accurately identifying regions of interest to count vehicles in parking lot images. The proposed system achieved 99.68% balanced accuracy on a custom dataset of 3,484 images, offering a cost-effective smart parking solution that ensures precise vehicle detection while preserving data privacy

Figures

Figures reproduced from arXiv: 2412.01983 by the authors.

Figure 1
Figure 1. System Architecture. There are significant differences in processing power, bandwidth usage, privacy, and cost when choosing the location for image processing. Taking that into account when designing a data pipeline is a necessary step in the system model proposal, choosing which data is captured, what is the processing at the edge and how the cloud deals with this data [30]. This can provide an architecture that be… view at source ↗
Figure 2
Figure 2. A sample picture of the dataset. Data labeling: We labeled the dataset, counting the number of vehicles in each image, including cars and trucks. This number of vehicles is deducted from the total number of places to obtain the amount of free spaces. Data pre-processing: Data was pre-processed by compressing the images from Portable Network Graphics (PNG) format to Joint Photographic Experts Group (JPG). Data filter… view at source ↗
Figure 3
Figure 3. Reference Mask used to select ROI . 4.3.1 Pre-Processing Mask Approach In the pre-processing approach, we utilized the reference mask to change the value of all pixels corresponding to the white region to gray in the three color channels (R = 128, G = 128, B= 128). This effectively covered the regions outside of the parking lot with gray, as can be seen in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Sample of the pre-processing approach. 4.3.2 Post-Processing Mask Approach The pre-processing approach modifies the image pixels, removing parts that can be important to provide context to the model, which is a factor that can improve the performance of YOLO models [46…
Figure 5
Figure 5. Figure 5: Sample of the post-processing approach. Although this post-processing method represents a novel approach for selecting a fully customized pixel-wise ROI for object detection, similar methods can be found in the Ultralytics library [40], particularly in the "Object Coun…
Figure 9
Figure 9. Figure 9: 12 [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 6
Figure 6. Figure 6: Balanced Accuracy by model and ROI method. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: Confusion Matrices for worst (YOLOv9t) to best (YOLOv10x) result on pre-processing method based on [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Confusion Matrices for worst (YOLOv9t) to best (YOLOv9e) result on post-processing method. [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 9
Figure 9. Figure 9: Qualitative analysis for the selected models [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 10
Figure 10. Figure 10: Average Processing time for each hardware and model. [PITH_FULL_IMAGE:figures/full_fig_p016_10.png]
Figure 11
Figure 11. Figure 11: Cost Comparison between Cameras and Sensors. [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.