基于弱监督语义分割的道路裂缝检测研究  

Study on Road Crack Detection Based on Weakly Supervised Semantic Segmentation

作　　者：赵卫东[1,2] 路明 张睿 ZHAO Weidong;LU Ming;ZHANG Rui(School of Software,Fudan University,Shanghai 200433,China;Shanghai Key Laboratory of Data Science,Shanghai 200433,China)

机构地区：[1]复旦大学软件学院,上海200433 [2]上海市数据科学重点实验室,上海200433

出　　处：《计算机科学》2024年第11期148-156,共9页Computer Science

基　　金：国家自然科学基金(71971066)。

摘　　要：基于弱监督语义分割的道路裂缝检测方法大多基于先分块后检测的流程,分块增加了标注的工作量和误判的分块数量。针对上述问题,提出了基于深度强化学习的道路裂缝分块分类模型,根据道路裂缝图像特点,对智能体的状态、动作和获取的奖励进行了设计,训练智能体自主选择裂缝分块,并将选择结果作为分块标签用于多尺寸分块道路裂缝检测。在cqu-bpdd等数据集上进行的对比实验,证明了所提方法在道路裂缝分割性能、裂缝平均宽度的测量准确度方面优于现有方法。Most of the existing weakly supervised semantic segmentation methods are based on the process of blocking before detection,which increases the annotation workload.However,the existing automatic block classification methods input all blocks into the model to predict the block category,increasing the number of blocks that are misjudged and affecting the performance of subsequent semantic segmentation.Aiming at the above problems,this paper proposes a road crack block classification model based on deep reinforcement learning.According to characteristics of road crack images,the states,actions,and rewards obtained by the agents are designed.The agent is trained to select crack blocks independently,and the selection results are used as block labels for multi-size block road crack detection.Through comparative experiments on several datasets,it is proved that the propsoed model outperforms existing methods in terms of road crack segmentation performance and crack width measurement accuracy.

关 键 词：道路裂缝检测 弱监督 语义分割 裂缝分块 深度强化学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]