Research on Semantic Segmentation Method of Road Scenes Based on Deep Learning

The accuracy of image semantic segmentation directly affects the ability of autonomous driving technology to perceive the surrounding environment. To address the problems of unclear object edge segmentation and inaccurate small target object segmentation in the semantic segmentation model of road images in deep learning, this paper combines the convolutional attention mechanism module with the multiscale feature fusion module to optimize and improve the Deeplabv3+ algorithm. The convolutional attention mechanism module is added to the feature extraction network to improve the network feature extraction capability. Feature enhancement and fusion operations are introduced in the encoder part to make features of different sizes deeper and more expressive. The model was experimentally and validated on the Cityscapes dataset, and the results showed that the method designed in this paper ensures segmentation efficiency while making object edge segmentation clearer and segmenting small target objects more accurate. The segmentation accuracy of the model has been improved.