中外公路2026，Vol.46Issue(4)：259-270,12.DOI:10.14048/j.issn.1671-2579.2026.04.028
改进YOLO模型的岩体节理特征快速检测与应用
Rapid detection and application of rock mass joint features based on improved YOLO model
摘要
Abstract
In view of the problems that existing monitoring methods for rock mass joints mainly rely on manual identification,with low detection efficiency and strong subjectivity,a detection and segmentation method for rock mass joints was proposed in this paper.The model improved the YOLOv8 algorithm by introducing the multi-scale feature module(MSBlock)and channel prior convolutional attention(CPCA)mechanism to enhance the image recognition accuracy of the existing YOLOv8 network model.By introducing the MSBlock algorithm,features at different scales and levels were fused,which could both capture the fine local details in the image and recognize the image features macroscopically.By introducing the CPCA mechanism,the attention distribution was adaptively adjusted according to the current image features,enabling the network to more flexibly deal with different targets.A total of 4 057 images collected from the AM Highway Project in Equatorial Guinea were trained and recognized.The results indicate that the performance evaluation indicators of the optimized algorithm,including Box(P),Box(R),Mask(P),Mask(R),rIOU,and CDICE,reach 89.4%,87.1%,61.6%,58.9%,92.8%,and 86.8%,respectively.Compared with the original model,these indicators increase by 4.6 percentage points,9.5 percentage points,8.5 percentage points,12.4 percentage points,17.5 percentage points,and 1.1 percentage points,respectively.This multi-scale feature fusion algorithm is more accurate and efficient when handling complex images and has better adaptability and robustness.关键词
岩体节理/图像识别/深度学习/YOLOv8/快速检测Key words
rock mass joint/image recognition/deep learning/YOLOv8/rapid detection分类
交通工程引用本文复制引用
李会兴,张敬,张健南,方皓,陈涛,王长帅..改进YOLO模型的岩体节理特征快速检测与应用[J].中外公路,2026,46(4):259-270,12.基金项目
中国路桥工程有限责任公司2023重点科技攻关项目(编号:2023-zlkj-08) （编号:2023-zlkj-08）
陕西省创新能力支撑计划——创新团队项目(编号:2020TD-005) （编号:2020TD-005）
