A street view semantic segmentation algorithm based on DeeplabV3+ architecture

In order to solve the problem of semantic segmentation difficulties of city street scene pictures due to uneven color and strong light changes, this paper proposes a semantic segmentation algorithm of city street scene pictures based on DeepLabV3+ architecture with dark channel a priori theory. The algorithm firstly inverts the city streetscape image and then performs color balancing through the dark channel a priori module, then extracts contextual information hierarchically using a multi-channel parallel network, fuses low-level features with high-level features hierarchically to obtain the optimized feature map, secondly completes the fusion of multi-scale features through the spatial pyramid structure, and finally fuses the obtained feature map with the decoder twice to generate the final prediction results. The experimental results show that the algorithm in this paper outperforms the original DeeplabV3+ model in terms of subjective perception and objective indicators in urban scenes, and provides a new solution and technical idea for semantic segmentation of urban scenes.