计算机工程2025，Vol.51Issue(9)：14-24,11.DOI:10.19678/j.issn.1000-3428.0069534
基于半监督学习的非结构化道路缺陷检测算法
Unstructured Road Defect Detection Algorithm Based on Semi-Supervised Learning
摘要
Abstract
Detecting defects on unstructured roads is important for road traffic safety;however,annotated datasets required for detection is limited.This study proposes the Multi-Augmentation with Memory(MAM)semi-supervised object detection algorithm to address the lack of annotated datasets for unstructured roads and the inability of existing models to learn from unlabeled data.First,a cache mechanism is introduced to store the positions of the bounding box regression information for unannotated images and images with pseudo annotations,avoiding computational resource wastage caused by subsequent matching.Second,the study proposes a hybrid data augmentation strategy that mixes the cached pseudo-labeled images with unlabeled images inputted into the student model,to enhance the model's generalizability to new data and balance the scale distribution of images.The MAM semi-supervised object detection algorithm is not limited by the object detection model and better maintains the consistency of object bounding boxes,thus avoiding the need to compute consistency loss.Experimental results show that the MAM algorithm is superior to other fully supervised and semi-supervised learning algorithms.On a self-built unstructured road defect dataset,called Defect,the MAM algorithm achieves improvements of 6.8,11.1,and 6.0 percentage points in terms of mean Average Precision(mAP)compared to those of the Soft Teacher algorithm in scenarios with annotation ratios of 10％,20％,and 30％,respectively.On a self-built unstructured road pothole dataset,called Pothole,the MAM algorithm achieves mAP improvements of 5.8 and 4.3 percentage points compared to those of the Soft Teacher algorithm in scenarios with annotation ratios of 15％and 30％,respectively.关键词
非结构化道路/缺陷目标检测/半监督学习/伪标签/缓存机制/混合数据增强Key words
unstructured road/defect object detection/semi-supervised learning/pseudo label/cache mechanism/hybrid data augmentation分类
信息技术与安全科学引用本文复制引用
朱思远,李佳圣,邹丹平,何迪,郁文贤..基于半监督学习的非结构化道路缺陷检测算法[J].计算机工程,2025,51(9):14-24,11.基金项目
国家自然科学基金重点项目(62231010) （62231010）
航天科技集团应用创新计划(6230109003). （6230109003）
