Assessing the performance of YOLOv5, YOLOv6, and YOLOv7 in road defect detection and classification: a comparative study

Mohd Yusof, Najiha ‘Izzaty; Sophian, Ali; Mohd Zaki, Hasan Firdaus; Bawono, Ali Aryo; Embong, Abd Halim; Ashraf, Arselan

doi:10.11591/eei.v13i1.6317

Cited by 6 publications

(1 citation statement)

References 16 publications

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“…The experimental results showed that as the proportion of injected noise increased, the performance of the YOLO algorithm gradually declined, especially when the noise ratio reached 100%, at which point performance significantly decreased, most objects could not be detected, and the detected object labels were also wrong. Mohd et al [39] evaluated and compared the performance of YOLOv5, YOLOv6, and YOLOv7 models in road defect detection and classification. The evaluation metrics used included the model's training time, mAP@0.5 (mean average precision), accuracy, and inference speed.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Evaluation Methods for Deep Learning Models in Target Detection for SAR Images

Wang,

Liu,

Zhou

et al. 2024

Remote Sensing

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As artificial intelligence technology advances, the application of object detection technology in the field of SAR (synthetic aperture radar) imagery is becoming increasingly widespread. However, it also faces challenges such as resource limitations in spaceborne environments and significant uncertainty in the intensity of interference in application scenarios. These factors make the performance evaluation of object detection key to ensuring the smooth execution of tasks. In the face of such complex and harsh application scenarios, methods that rely on single-dimensional evaluation to assess models have had their limitations highlighted. Therefore, this paper proposes a multi-dimensional evaluation method for deep learning models used in SAR image object detection. This method evaluates models in a multi-dimensional manner, covering the training, testing, and application stages of the model, and constructs a multi-dimensional evaluation index system. The training stage includes assessing training efficiency and the impact of training samples; the testing stage includes model performance evaluation, application-based evaluation, and task-based evaluation; and the application stage includes model operation evaluation and model deployment evaluation. The evaluations of these three stages constitute the key links in the performance evaluation of deep learning models. Furthermore, this paper proposes a multi-indicator comprehensive evaluation method based on entropy weight correlation scaling, which calculates the weights of each evaluation indicator through test data, thereby providing a balanced and comprehensive evaluation mechanism for model performance. In the experiments, we designed specific interferences for SAR images in the testing stage and tested three models from the YOLO series. Finally, we constructed a multi-dimensional performance profile diagram for deep learning object detection models, providing a new visualization method to comprehensively characterize model performance in complex application scenarios. This can provide more accurate and comprehensive model performance evaluation for remote sensing data processing, thereby guiding model selection and optimization. The evaluation method proposed in this study adopts a multi-dimensional perspective, comprehensively assessing the three core stages of a model’s lifecycle: training, testing, and application. This framework demonstrates significant versatility and adaptability, enabling it to transcend the boundaries of remote sensing technology and provide support for a wide range of model evaluation and optimization tasks.

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Section: Introductionmentioning

confidence: 99%

Multidimensional Evaluation Methods for Deep Learning Models in Target Detection for SAR Images

Wang,

Liu,

Zhou

et al. 2024

Remote Sensing

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YOLO-Claw: A fast and accurate method for chicken claw detection

Wu,

Ying,

Zhou

et al. 2024

Engineering Applications of Artificial Intelligence

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Deep learning for detection and counting of Nephrops norvegicus from underwater videos

Burguera,

Bonin-Font,

Chatzievangelou

et al. 2024

ICES Journal of Marine Science

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The Norway lobster (Nephrops norvegicus) is one of the most important fishery items for the EU blue economy. This paper describes a software architecture based on neural networks, designed to identify the presence of N. norvegicus and estimate the number of its individuals per square meter (i.e. stock density) in deep-sea (350–380 m depth) Fishery No-Take Zones of the northwestern Mediterranean. Inferencing models were obtained by training open-source networks with images obtained from frames partitioning of in submarine vehicle videos. Animal detections were also tracked in successive frames of video sequences to avoid biases in individual recounting, offering significant success and precision in detection and density estimations.

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Assessing the performance of YOLOv5, YOLOv6, and YOLOv7 in road defect detection and classification: a comparative study

Cited by 6 publications

References 16 publications

Multidimensional Evaluation Methods for Deep Learning Models in Target Detection for SAR Images

Multidimensional Evaluation Methods for Deep Learning Models in Target Detection for SAR Images

YOLO-Claw: A fast and accurate method for chicken claw detection

Deep learning for detection and counting of Nephrops norvegicus from underwater videos

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