Study on Road Crack Detection Based on Weakly Supervised Semantic Segmentation

Abstract

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.