Research Article
Study on Pixel Level Segmentation and Area Quantification of Highway Slope Cracks
@INPROCEEDINGS{10.1007/978-3-030-90196-7_3, author={YunLing Zhang and Pei Guo and Pengyu Liu and Yaoyao Li and Shanji Chen}, title={Study on Pixel Level Segmentation and Area Quantification of Highway Slope Cracks}, proceedings={Artificial Intelligence for Communications and Networks. Third EAI International Conference, AICON 2021, Xining, China, October 23--24, 2021, Proceedings, Part I}, proceedings_a={AICON}, year={2021}, month={11}, keywords={Crack Pixel area Actual area}, doi={10.1007/978-3-030-90196-7_3} }- YunLing Zhang
Pei Guo
Pengyu Liu
Yaoyao Li
Shanji Chen
Year: 2021
Study on Pixel Level Segmentation and Area Quantification of Highway Slope Cracks
AICON
Springer
DOI: 10.1007/978-3-030-90196-7_3
Abstract
Highway slope disasters show obvious stage characteristics before the occurrence, and cracks are the early symptoms of most highway slope disasters. Computer vision is widely used in crack detection because of its advantages of high efficiency and low cost. In view of the shortage of traditional crack actual area calculation methods and poor effect, this paper proposes a slope crack pixel level segmentation method based on deep convolutional neural network, so as to generate accurate segmentation of crack morphology. Then, according to the binary segmentation mask, the checkerboard mapping method is proposed to calculate the actual crack area. Finally, the effectiveness and superiority of the proposed checkerboard mapping method are verified and evaluated with a self-made data set of highway crack image. The experimental results show that this method can effectively detect the actual crack area, and the relative error is small. The calculated results can be used as a reference for slope disaster warning.
