一种基于改进YOLOv8的轻量化路面病害检测算法  

A refined YOLOv8-based algorithm for lightweight pavement disease detection

作　　者：胡凤阔 叶兰 谭显峰 张钦展 胡志新 方清 王磊 满孝锋 HU Fengkuo;YE Lan;TAN Xianfeng;ZHANG Qinzhan;HU Zhixin;FANG Qing;WANG Lei;MAN Xiaofeng(School of Mechanical and Electronic Engineering,East China University of Technology,Nanchang Jiangxi 330013,China;Jiangxi Traffic Engineering Quality Supervision Station Test Center,Nanchang Jiangxi 330006,China;China National Chemical Communications Construction Group Second Engineering Co.LTD,Qingdao Shandong 266000,China)

机构地区：[1]东华理工大学机械与电子工程学院,江西南昌330013 [2]江西省交通工程质量监督站试验检测中心,江西南昌330006 [3]中化学交通建设集团第二工程有限公司,山东青岛266000

出　　处：《图学学报》2024年第5期892-900,共9页Journal of Graphics

基　　金：江西省交通运输厅科技项目(2023H0031);博士科研启动基金项目(DHBK2023007)。

摘　　要：路面病害检测是实现道路损伤修复、确保行车安全的关键任务。针对现有路面病害检测算法精度低、成本高、模型参数大及难以应用于移动终端设备等问题,提出了一种基于改进YOLOv8n模型的轻量级检测算法YOLOv8n-GSBP。首先,通过在骨干网络引入C2f-GhostNetv2模块保证检测精度并实现了模型轻量化,同时在SPPF模块后加入SimAM注意力机制模块,增强了网络对路面病害特征提取与背景环境特征区分的能力;其次,通过在颈部网络更换BiFPN结构增强模型多尺度特征融合能力,提升精确度和鲁棒性的同时解决了路面病害尺度差异较大问题;最后,基于参数共享原理改进检测头,并引入空间通道重建卷积模块SCConv,实现了检测头的轻量化,降低了模型参数和计算量。在RDD2022数据集上的实验结果表明,YOLOv8n-GSBP路面病害检测方法相较于YOLOv8n网络mAP50虽只提高了0.3%,但参数量降低了55.6%、计算量大幅度降低至36.7%,实现了对道路病害的实时准确检测。通过与其他主流目标检测算法的对比,进一步验证了算法的有效性和优越性。Road surface defect detection is a crucial task for repairing road damage and ensuring driving safety.To address the issues of low detection accuracy,high costs,large model parameters,and the difficulty in applying existing road surface defect detection algorithms to mobile terminal devices,a lightweight detection algorithm,YOLOv8n-GSBP,based on the improved YOLOv8n model,was proposed.Firstly,the C2f-GhostNetv2 module was introduced into the backbone network to maintain detection accuracy while achieving model lightweight.Additionally,the SimAM module was added after the SPPF module to enhance the network’s ability to extract road surface defect features and distinguish them from background environmental features.Secondly,the neck network was replaced with the BiFPN structure,and the model’s multi-scale feature fusion capability was enhanced while addressing significant differences in road surface defect scales to improve precision and robustness.Finally,the head was improved by the parameter-sharing principle,and the spatial channel reconstruction convolutional module SCConv was introduced to achieve lightweight improvement of the detection head while reducing model parameters and computational complexity.The experimental results on the RDD2022 dataset showed that the mAP50 of YOLOv8n-GSBP road surface disease detection method was 0.3%higher than that of the YOLOv8n;however,the parameters were reduced by 55.6%and the computational complexity was reduced to 36.7%.Furthermore,through comparisons with other mainstream object detection algorithms,we further validated both effectiveness and superiority of our proposed algorithm.

关 键 词：深度学习 路面病害检测 YOLOv8n 注意力机制 轻量级算法 

分 类 号：U418.6[交通运输工程—道路与铁道工程] TP391.4[自动化与计算机技术—计算机应用技术]