Inicio  /  Applied Sciences  /  Vol: 13 Par: 24 (2023)  /  Artículo
ARTÍCULO
TITULO

Swin-APT: An Enhancing Swin-Transformer Adaptor for Intelligent Transportation

Yunzhuo Liu    
Chunjiang Wu    
Yuting Zeng    
Keyu Chen and Shijie Zhou    

Resumen

Artificial Intelligence has been widely applied in intelligent transportation systems. In this work, Swin-APT, a deep learning-based approach for semantic segmentation and object detection in intelligent transportation systems is presented. Swin-APT includes a lightweight network and a multiscale adapter network designed for image semantic segmentation and object detection tasks. An inter-frame consistency module is proposed to extract more accurate road information from images. Experimental results on four datasets: BDD100K, CamVid, SYNTHIA, and CeyMo, demonstrate that Swin-APT outperforms the baseline by 13.1%. Furthermore, experiments on the road marking detection benchmark show an improvement of 1.85% of mAcc.

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