PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
Semantic segmentation is a technique for classifying images pixel by pixel. The road surface cracks are difficult to extract with traditional method because they are exposed to more environmental factors such as light and more interference noise. This paper proposes a road surface crack detection technology based on RU-Net. The impact of environmental factors is effectively reduced by this network, and it realizes the classification of cracks from end to end and pixel by pixel. The network mainly includes two parts which are encoder and decoder. The encoder part is mainly used for feature extraction, and the decoder part is mainly used to recover spatial information. The results of the experiment show that the RU-Net achieves an accuracy of more than 98% and an MIoU of more than 73%.
Fei Hu,Jiahang Liu,Donghao Yang, andRongtao Liu
"Semantic segmentation of road surface crack images using RU-Net model", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 1172014 (27 January 2021); https://doi.org/10.1117/12.2589438
ACCESS THE FULL ARTICLE
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.