Lightweight Road Damage Detection Method Based on Improved YOLOv8

doi:10.3778/j.issn.1002-8331.2402-0243

Abstract

Abstract: Aiming at the problems of large memory space occupation, high computational complexity, and difficult to meet the real-time target detection requirements of the road damage detection model in complex scenes, a lightweight road damage detection model DGE-YOLO-P is proposed for the complex natural scenes. Firstly, the C2f fusion deformable convolutional design C2f_DCNv3 module in the network is enhanced to enhance the modelling capability of object deformation and the input feature information is dimensionality reduced to effectively reduce the number of parameters and the computational complexity. The input feature information is dimensionality reduced to effectively reduce the number of model parameters and computational complexity. Then, the GS-Decoupled head detection module is designed to reduce the parameters of the detection head while realising the effective aggregation of global information. At the same time, the E-Slide Loss weight function is designed to assign higher weights to the difficult samples, fully learn the difficult sample data in road damage, and further improve the model detection accuracy. Finally, channel pruning is used to reduce the redundant channels of the model, which effectively compresses the model volume and improves the detection speed. The experimental results show that the mAP of the DGE-YOLO-P model is increased by 2.4?percentage points compared with the YOLOv8n model, while the number of model parameters, computational volume and model size are reduced by 58.1%, 66.7% and 55.5%, respectively. The detection speed FPS is increased from 34 frame/s to 51 frame/s.

Key words: road damage detection, complex scene, YOLOv8n, lightweight, model pruning

摘要： 针对复杂场景下道路病害检测模型占用内存空间大、计算复杂度高和检测速度难以满足实时目标检测要求等问题，提出一种面向复杂自然场景的轻量级道路病害检测模型DGE-YOLO-P。将网络中的C2f融合可变形卷积设计C2f_DCNv3模块增强对物体形变的建模能力，并对输入特征信息进行降维处理，有效降低模型参数量和计算复杂度。设计GS-Decoupled head检测模块，降低检测头参数的同时实现全局信息的有效聚合。同时，设计E-Slide Loss权重函数，为困难样本分配更高权重，充分学习道路病害中的难样本数据，进一步提高模型检测精度。采用通道剪枝减少模型冗余通道，有效压缩模型体积并提高检测速度。实验结果表明，DGE-YOLO-P模型相较于YOLOv8n模型mAP提高2.4个百分点，而模型参数量、计算量和模型大小分别降低58.1%、66.7%和55.5%。检测速度FPS由34帧/s提高到51帧/s。

关键词: 道路病害检测, 复杂场景, YOLOv8n, 轻量化, 模型剪枝

XU Tiefeng, HUANG He, ZHANG Hongmin, NIU Xiaofu. Lightweight Road Damage Detection Method Based on Improved YOLOv8[J]. Computer Engineering and Applications, 2024, 60(14): 175-186.

胥铁峰, 黄河, 张红民, 牛晓富. 基于改进YOLOv8的轻量化道路病害检测方法[J]. 计算机工程与应用, 2024, 60(14): 175-186.

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