Abstract: The proportional integration differentiation(PID) semantic segmentation architecture mitigates the problem of overshooting in the dual-branch architecture,where fine-grained features are easily overwhelmed by surrounding contextual information.However,the high-resolution boundary branch in this architecture significantly impacts the inference speed.To address this issue,an efficient PID architecture based on spatial attention mechanisms and a lightweight auxiliary semantic branch is proposed.The designed lightweight attention fusion module is used to extract precise contextual information and guide the fusion of various feature information.Additionally,a fast aggregation pyramid pooling module is introduced to rapidly aggregate semantic information across multiple scales.Finally,a deep supervision training strategy,combined with the canny edge detection operator,is designed to enhance the training effectiveness.In comparison to the baseline,the proposed model achieves a 6% increase in accuracy at the cost of a slightly increased latency.It strikes a good balance between accuracy and speed on the Cityscapes,CamVid,and KITTI datasets,outperforming existing models in the same speed range.Notably,the model achieves an accuracy of 78.5% at 120.9 frames/s on the Cityscapes test set.

Key words: Real-time semantic segmentation, Autonomous driving, Overshoot, Spatial attention mechanism, Edge detection