Crack-Detection Method for Asphalt Pavement Based on the Improved YOLOv5

Tang, Gangting; Yin, Chao; Zhang, Xixuan; Liu, Xinliang; Li, Shufeng

doi:10.1061/jpcfev.cfeng-4615

Cited by 1 publication

(1 citation statement)

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“…However, the model handles large-scale detection layers, which may increase the computational complexity and affect real-time performance. Tang et al (2024) proposed a crack detection algorithm based on improved YOLOv5s for asphalt pavement crack detection under complex pavement conditions (affected by glare, road surface water, debris, etc.) with low recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

EMG-YOLO: road crack detection algorithm for edge computing devices

Xing,

Han,

Pan

et al. 2024

Front. Neurorobot.

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IntroductionRoad cracks significantly shorten the service life of roads. Manual detection methods are inefficient and costly. The YOLOv5 model has made some progress in road crack detection. However, issues arise when deployed on edge computing devices. The main problem is that edge computing devices are directly connected to sensors. This results in the collection of noisy, poor-quality data. This problem adds computational burden to the model, potentially impacting its accuracy. To address these issues, this paper proposes a novel road crack detection algorithm named EMG-YOLO.MethodsFirst, an Efficient Decoupled Header is introduced in YOLOv5 to optimize the head structure. This approach separates the classification task from the localization task. Each task can then focus on learning its most relevant features. This significantly reduces the model’s computational resources and time. It also achieves faster convergence rates. Second, the IOU loss function in the model is upgraded to the MPDIOU loss function. This function works by minimizing the top-left and bottom-right point distances between the predicted bounding box and the actual labeled bounding box. The MPDIOU loss function addresses the complex computation and high computational burden of the current YOLOv5 model. Finally, the GCC3 module replaces the traditional convolution. It performs global context modeling with the input feature map to obtain global context information. This enhances the model’s detection capabilities on edge computing devices.ResultsExperimental results show that the improved model has better performance in all parameter indicators compared to current mainstream algorithms. The EMG-YOLO model improves the accuracy of the YOLOv5 model by 2.7%. The mAP (0.5) and mAP (0.9) are improved by 2.9% and 0.9%, respectively. The new algorithm also outperforms the YOLOv5 model in complex environments on edge computing devices.DiscussionThe EMG-YOLO algorithm proposed in this paper effectively addresses the issues of poor data quality and high computational burden on edge computing devices. This is achieved through optimizing the model head structure, upgrading the loss function, and introducing global context modeling. Experimental results demonstrate significant improvements in both accuracy and efficiency, especially in complex environments. Future research can further optimize this algorithm and explore more lightweight and efficient object detection models for edge computing devices.

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Section: Introductionmentioning

confidence: 99%