Semantic segmentation of road surface crack images using RU-Net model

Fei Hu; Jiahang Liu; Donghao Yang; Rongtao Liu

doi:10.1117/12.2589438

27 January 2021 Semantic segmentation of road surface crack images using RU-Net model

Fei Hu, Jiahang Liu, Donghao Yang, Rongtao Liu

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 1172014 (2021) https://doi.org/10.1117/12.2589438
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

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%.

Citation Download Citation

Fei Hu, Jiahang Liu, Donghao Yang, and Rongtao 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

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