Research on Lightweight Road Semantic Segmentation Algorithm Based on DeepLabv3+

A lightweight image semantic segmentation model called MCDF is proposed based on the DeepLabv3+ to balance accuracy in light of the issues such as unclear boundary segmentation and misjudgments in road segmentation for mobile terminals like wheeled robots during autonomous driving, as well as the requirement of lightweight in practical application scenarios. The improvements mainly focus on two aspects: lightweight and accuracy. In terms of lightweight, the backbone network is replaced with the lightweight MobileNetV2 network, and the regular convolutions in the ASPP module are replaced with Depthwise Separable Convolutions, reducing the number of computations. In terms of accuracy, the Coordinate Attention is introduced after the backbone network, and then a feature enhancement extraction structure is concatenated at the decoder end to enrich boundary information. Finally, the mIoU obtained on the Cityscapes dataset is 72.68%, with only a 2.86% decrease. The model size is approximately 1/14 of the original, measuring only 14.84 MB. This achieves a balance between lightweight design and accuracy, effectively meeting the requirements for outdoor road scene segmentation.