Lightweight Model for Pavement Defect Detection Based on Improved YOLOv7

Huang, Peng; Wang, Shenghuai; Chen, Jianyu; Li, Weijie; Xiao, Peng

doi:10.3390/s23167112

Cited by 8 publications

(2 citation statements)

References 22 publications

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“…integrated the Ghost Conv module to improve YOLOv7, enhancing feature extraction while reducing parameters and computation. [ 22 ] Xing et al. combined FasterNet with an attention mechanism and introduced lightweight GSConv convolution to enhance feature extraction and fusion.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight YOLO Object Detection Algorithm Based on Bidirectional Multi‐Scale Feature Enhancement

Liu,

Zhang,

Zhang

et al. 2024

Advcd Theory and Sims

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This paper proposes a lightweight YOLO object detection algorithm based on bidirectional multi‐scale feature enhancement. The problem is that the original YOLOv5 algorithm does not make full use of the relationship between the feature layers, resulting in the loss of target semantic information and a large number of parameters. First, a bidirectional multi‐scale feature‐enhanced weighted fusion backbone network is constructed to extract target features repeatedly. It enhances the fusion ability of shallow detail features and high‐level semantic information to capture richer multi‐scale semantic information. Second, the NCA attention module is built and integrated into the feature fusion network to enhance the critical characteristics of the target region. Finally, the Ghost module is used instead of the convolutional blocks in the original network to lighten the model while reducing the network complexity and training difficulty. Experimental results show that the improved YOLOv5 algorithm achieves 78.8% mAP@0.5 for the PASCAL VOC2012 dataset, which is 1.5% higher than the original algorithm, at 62.5 FPS. The number of parameters is also reduced by 43.6%. The mAP@0.5 on the self‐made metal foreign object dataset reached 98.4%, at 58.8 FPS, which can meet the requirements of end‐device deployment and real‐time detection.

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Section: Introductionmentioning

confidence: 99%

A Lightweight YOLO Object Detection Algorithm Based on Bidirectional Multi‐Scale Feature Enhancement

Liu,

Zhang,

Zhang

et al. 2024

Advcd Theory and Sims

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“…It achieved 73.3% mAP and 41FPS inference speed, surpassing existing models. Huang P. et al [20] proposed a lightweight pavement defect detection model based on an improved YOLOv7 architecture, achieving 91% average accuracy with significant reductions in parameters and computations, making it suitable for edge terminal devices. Liu Y. et al [21] proposed an optimized road defect detection model based on YOLOv5s, enhancing speed and precision in detecting on the GRDDC dataset while reducing the model size.…”

Section: Introductionmentioning

confidence: 99%

Road Surface Defect Detection Algorithm Based on YOLOv8

Sun,

Zhu,

Qin

et al. 2024

Electronics

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In maintaining roads and ensuring safety, promptly detecting and repairing pavement defects is crucial. However, conventional detection methods demand substantial manpower, incur high costs, and suffer from low efficiency. To enhance road maintenance efficiency and reduce costs, we propose an improved algorithm based on YOLOv8. Our method incorporates several key enhancements. First, we replace conventional convolutions with a module composed of spatial-to-depth layers and nonstrided convolution layers (SPD-Conv) in the network backbone, enhancing the capability of recognizing small-sized defects. Second, we replace the neck of YOLOv8 with the neck of the ASF-YOLO network to fully integrate spatial and scale features, improving multiscale feature extraction capability. Additionally, we introduce the FasterNet block from the FasterNet network into C2f to minimize redundant computations. Furthermore, we utilize Wise-IoU (WIoU) to optimize the model’s loss function, which accounts for the quality factors of objects more effectively, enabling adaptive learning adjustments based on samples of varying qualities. Our model was evaluated on the RDD2022 road damage dataset, demonstrating significant improvements over the baseline model. Specifically, with a 2.8% improvement in mAP and a detection speed reaching 43 FPS, our method proves to be highly effective in real-time road damage detection tasks.

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