Computer Vision-based Method for Monitoring Road Slope Cracks

An automatic monitoring method for highway slope cracks was proposed, aiming to timely detect crack problems and reduce potential hazards. Taking highway slope cracks as the research object, for the characteristics of irregularity of crack image pattern and large interference of surrounding environment, a highway slope cracks segmentation network (SCSNet) and a highway slope crack geometric parameter calculation method were designed. An encoder was used to gradually capture higher-level semantic features, and a decoder was used to fuse information between different scales by gradually recovering spatial information and combining jump connections. Then, for the case of complex road slope crack images and other situations, a channel attention mechanism was used to learn the features between channels and enhance the feature representation of cracks. A method based on the connectivity domain analysis was proposed to obtain the crack connectivity domain and calculate the crack length, width and area geometric parameters. Results show that the average intersection ratio of the segmentation network reaches 87.86%, which can extract the highway slope crack features better, and the current state of the cracks can be measured more accurately using the crack geometric parameter calculation method.