Abstract:

Affected by factors such as changes in lighting conditions and image distortion in highway tunnels， the difficulty of extracting features at different scales from lining crack images has increased， which has affected the recognition of small cracks in highway tunnel lining. Therefore， a method for identifying of small cracks in highway tunnel lining based on deep learning SSD algorithm is proposed in this paper. Firstly， using the weighted average method to perform grayscale processing on highway tunnel images， extracting line segment features from the images through line segment detection algorithms， obtaining edge detection results of the image in both horizontal and vertical directions using Sobel operators， and correcting distorted images based on perspective geometry principles； secondly， introducing deep learning SSD algorithm， using regression model to adjust the position of prior boxes in SSD algorithm， and predicting the category of each prior box on the effective feature layer， combining non maximum suppression technology， the optimal prediction box is selected to achieve accurate identification of cracks. Experimental results show that the lining cracks identiflcation by this method are basically consistent with the cracks in tunnel images， with an AUC value of 0.956， which can effectively improve the accuracy of identifying small cracks in highway tunnel lining and have better application performance.

Key words: deep learning SSD algorithm, highway tunnel, small cracks in the lining, identificat