Research on semantic segmentation algorithm of road pollution based on deep learning

Xin Zhang; Yan Zhu; Li Deng; Long Qi; Tao Zhang; Zhihao Tan

doi:10.1117/12.2674939

23 May 2023 Research on semantic segmentation algorithm of road pollution based on deep learning

Xin Zhang, Yan Zhu, Li Deng, Long Qi, Tao Zhang, Zhihao Tan

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 1260441 (2023) https://doi.org/10.1117/12.2674939
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

Abstract

Road pollution identification is a very important link in keeping the road clean and the safe running of vehicles. In the process of mine operation, it is especially easy to produce sediment and other garbage to cause road pollution. However, currently widely used pollution detection methods are not very accurate segmentation of road pollution. In this paper, an efficient semantic segmentation algorithm EEDNet based on codecing and decoding structures is proposed to solve the detection and segmentation problem of mine road pollution. The designed pollution segmentation model is trained through the data set collected by the cameras arranged in the mine monitoring area, and a better pollution segmentation effect is obtained. Especially compared with the most advanced pollution segmentation methods, the model designed by us greatly improves the accuracy of road pollution segmentation, and provides road information support and safety guarantee for the safe driving of road vehicles in mine working areas.

Citation Download Citation

Xin Zhang, Yan Zhu, Li Deng, Long Qi, Tao Zhang, and Zhihao Tan "Research on semantic segmentation algorithm of road pollution based on deep learning", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 1260441 (23 May 2023); https://doi.org/10.1117/12.2674939

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