The Dynamic Image Analysis of Retaining Wall Crack Detection and Gap Hazard Evaluation Method with Deep Learning

Abstract

This study uses machine vision combined with drones to detect cracks in retaining walls in mountaineering areas or forest roads. Using the drone’s pre-collected images of retaining walls, the gaps in the wall are obtained as the target for sample data. Deep learning is carried out with neural network architecture. After repeated training of the module, the characteristic conditions of the crack are extracted from the image to be tested. Then, the various characteristics of the gap feature are extracted through image conversion, and the factors are analyzed to evaluate the danger degree of the gap. This study proposes a series of gap danger factor equations for the gap to analyze the safety of the detected gap image so that the system can judge the image information collected by the drone to assist the user in evaluating the safety of the gap. At present, deep learning modules and gap hazard evaluation methods are used to make suggestions on gaps. The expansion of the database has effectively improved the efficiency of gap identification. The detection process is about 20–25 frames per second, and the processing time is about 0.04 s. During the capture process, there will still be a few misjudgments and improper circle selections. The misjudgment rate is between 2.1% and 2.6%.

Keywords

• machine vision
• drones
• retaining walls
• crack detection
• neural network architecture
• gap hazard evaluation method