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

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

Blog Article

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 621 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 Apple Corer the road marking detection benchmark show an improvement of 1.85% of mAcc.