液晶与显示2026，Vol.41Issue(8)：1158-1174,17.DOI:10.3724/CJLCD.2026-0119
SimCLR-YOLO:面向低标注依赖的公路病害检测算法
Sim CLR-YOLO:a road disease detection algorithm for low annotation dependence
摘要
Abstract
To address the challenges in road disease detection,such as sample scarcity,high miss rates for weakly textured targets,and complex background interference,a road disease detection algorithm named SimCLR-YOLO is proposed to integrate unsupervised pre-training SimCLR with YOLOv8n.Firstly,the SimCLR backbone is replaced with YOLOv8n's CSPDarknet to achieve structural alignment and seamless feature transfer,resolving the feature loss issue caused by network structure mismatch.Secondly,masked reconstruction is incorporated into contrastive learning,forcing the encoder to focus on the texture contours of cracks and potholes,thereby compensating for the under-fitting of contrastive learning to weakly textured features.Finally,a CBAM attention module is embedded to guide the model in suppressing background noise such as road texture and shadows during the pre-training phase,while enhancing feature responses in critical regions.Experimental results on the public RDD2022 dataset show that the SimCLR-YOLO algorithm achieves an mAP@0.5 of 89.4%,which is 1.7%higher than the baseline model,with a 1.1%improvement in mAP@0.5:0.95 and a 1.8%increase in F1-score.Validation under different annotation ratios demonstrates that the scheme incorporating pre-trained weights outperforms training from scratch across all data gradients.At a 50%annotation ratio,the SimCLR-YOLO algorithm still achieves an mAP@0.5 of 87%,approaching the baseline model under full annotation,significantly reducing the dependence on annotated data.Moreover,the improvements are only effective during pre-training,with no additional overhead in inference parameters(3.01M),providing an effective technical solution for real-time road disease detection under sample-scarce conditions.关键词
公路病害/YOLOv8n/SimCLR/无监督预训练/低标注依赖/预训练Key words
road disease/YOLOv8n/SimCLR/unsupervised pre-training/low annotation dependence/pre-training分类
信息技术与安全科学引用本文复制引用
向天明,燕芳,李嘉辉,孙瑞..SimCLR-YOLO:面向低标注依赖的公路病害检测算法[J].液晶与显示,2026,41(8):1158-1174,17.基金项目
内蒙古自然科学基金(面上项目)(No.2025MS06002) （面上项目）
内蒙古自治区重点研发和成果转化计划(No.2025SYFHH0062)Supported by Natural Science Foundation of Inner Mongolia Autonomous Region(No.2025MS06002) （No.2025SYFHH0062）
Key Research and Development and Achievement Transformation Program of Inner Mongolia Autonomous Region(No.2025SYFHH0062) （No.2025SYFHH0062）
