A traffic image semantic segmentation algorithm based on UNET

Chunli Wang; Botao Zeng; Jindie Gao; Ge Peng; Wei Yang

doi:10.1117/12.2671074

28 April 2023 A traffic image semantic segmentation algorithm based on UNET

Chunli Wang, Botao Zeng, Jindie Gao, Ge Peng, Wei Yang

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126101X (2023) https://doi.org/10.1117/12.2671074
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

In recent years, the traffic image semantic segmentation plays a crucial role in automatic driving. The result of semantic segmentation will directly affect the car's understanding of the external scene. Thus, a semantic segmentation algorithm based on UNET network model is proposed for getting better results in traffic images segmentation. To prove the effectiveness of the proposed algorithm, highway driving dataset is used on the experiments. The experimental results show that the proposed network can achieve high precision image semantic segmentation in complex road scenes, and the segmentation accuracy is greatly improved compared with other network models.

Citation Download Citation

Chunli Wang, Botao Zeng, Jindie Gao, Ge Peng, and Wei Yang "A traffic image semantic segmentation algorithm based on UNET", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126101X (28 April 2023); https://doi.org/10.1117/12.2671074

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