Lightweight Semantic Segmentation of Road Scenes for Autonomous Driving.

In the field of autonomous driving, existing semantic segmentation algorithms for road scenes have huge overhead and cannot meet the real-time performance of autonomous driving. Based on the overall structure of DeepLabV3+, this paper proposes a lightweight image semantic segmentation model with parallel feature processing, which takes into account both high accuracy and real-time performance. Firstly, MobileNetV2 is used as the backbone network to streamline the upsampling process and reduce the number of network parameters for network migration and training. Then, a dual-attention mechanism is introduced to combine with the null convolutional space pyramid module to form a parallel feature processing structure to improve segmentation accuracy. Finally, the parallel feature processing structure is then combined with MobileNetV2 to complete the extraction of image features. The experimental results show that the proposed model can guarantee efficient and accurate image segmentation with less system overhead and number of network parameters than the traditional model. The model achieves 73.61% mIoU in the Cityscapes dataset and processes a 512 x 512 image in 25 ms. [ABSTRACT FROM AUTHOR]