Improved YOLOv8 algorithm for detecting cracks in roadbed slopes

Niu Xiaofu; Huang He; Zhang Hongmin; Xu Tiefeng

doi:10.12086/oee.2024.240171

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Niu X F, Huang H, Zhang H M, et al. Improved YOLOv8 algorithm for detecting cracks in roadbed slopes[J]. Opto-Electron Eng, 2024, 51(11): 240171. doi: 10.12086/oee.2024.240171

Citation:

Niu X F, Huang H, Zhang H M, et al. Improved YOLOv8 algorithm for detecting cracks in roadbed slopes[J]. Opto-Electron Eng, 2024, 51(11): 240171. doi: 10.12086/oee.2024.240171

Improved YOLOv8 algorithm for detecting cracks in roadbed slopes

1.
School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China
2.
China Merchants Chongqing Transportation Research and Design Institute Limited, Chongqing 400067, China

Fund Project: Project supported by National Key Research and Development Program (2022YFC3002603), Chongqing Natural Science Foundation Top Project (cstc2021 jcyj-msxmX0525, CSTB2022NSCQ-MSX0786, CSTB2023NSCQ-MSX0911), and Science and Technology Research Project of Chongqing Municipal Education Commission (KJQN202201109)

More Information

^*Corresponding author: hmzhang@cqut.edu.cn
CSTR: 32245.14.oee.2024.240171

Received Date 20 July 2024

Revised Date 24 October 2024

Accepted Date 24 October 2024

Published Date 25 November 2024

Abstract

Abstract

An improved YOLOv8 algorithm is proposed to address the problems of low detection accuracy and weak generalization ability in existing roadbed slope crack detection algorithms. Firstly, a reparameterization module is embedded in the backbone network to lighten the model while capturing crack details and global information, improving detection accuracy of the model. Secondly, the C2f-GD module is designed to achieve efficient fusion of model features and enhance the generalization ability of the model. Finally, the lightweight detection head L-GNHead is designed to improve the crack detection accuracy for different scales, while the SIoU loss function is used to accelerate model convergence. The experimental results on the self-constructed roadbed slope crack dataset show that the improved algorithm improves mAP50 and mAP50-95 by 3.3% and 2.5% respectively, reduces parameters and computational costs by 46.6% and 44.4% respectively, and improves FPS by 18 frames/s compared with the original algorithm. The generalization validation results on the dataset RDD2022 show that the improved algorithm not only achieves higher detection accuracy, but also faster detection speed.
- YOLOv8 /
- slope cracks /
- reparameterization /
- C2f-GD /
- L-GNHead /
- SIoU

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References

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Overview

Overview

The road transportation network in our country is constantly optimizing, and the slope engineering of highways is a key link to ensure the safety and stability of roadbeds, and its importance is becoming increasingly prominent. Cracks, as the initial signs of most highway slope diseases, their increase, expansion, and evolution are intuitive manifestations of slope instability. Therefore, timely and accurate identification of these roadbed slope cracks is the significant for real-time monitoring and warning of highway slope disasters, as well as ensuring smooth and safe traffic. Traditional slope crack detection mainly relies on manual inspection, resulting in high detection costs and low efficiency. In recent years,while deep learning based object detection algorithms can identify cracks, they tend to be limited to a single simple scene. However, due to differences in the shape of slope cracks, complex backgrounds, and lighting conditions, there are problems such as low detection accuracy, complex network models that are difficult to meet real-time requirements, and poor model generalization. In response to the problems in current slope crack detection algorithms, this paper proposes an improved YOLOv8 algorithm roadbed slope crack detection algorithm. Firstly, a reparameterization module is embedded in the backbone network to enhance the network's feature extraction ability and improve the detection accuracy of the model. Then, a lightweight C2f-GD module is built in the neck network, which enhances the generalisation ability of the model. In addition, the lightweight detection head L-GNHead is designed, which greatly reduces the complexity of the model and improves the detection accuracy of slope cracks at different scales. Finally, the SIoU loss function is used to accelerate the model convergence and improve the detection accuracy. The experiment results show that the improved algorithm improves mAP50 and mAP50-95 by 3.3% and 2.5% respectively on the self-constructed slope crack dataset, effectively reducing missed and false detections of slope cracks. At the same time, the number of parameters and computational complexity of the model is reduced by 46.6% and 44.4% respectively, and the FPS is improved by 18 frames/s. In addition, this paper conducts generalization validation on the public dataset RDD2022, and the comprehensive results show that the improved algorithm makes the model more lightweight and efficient, which helps promote deployment on edge devices. The next step focuses on the deployment of the model on mobile devices and in-depth exploration based on the actual detection performance to better meet the needs of high-accuracy and real-time applications.