Traffic Street Scene Instance Segmentation Based on Adaptive Regulatory Convolution and Dual-Path Information Embedding

Instance-level segmentation of street scene is a key technology that cannot be ignored in unmanned driving. Aiming at the problems of dense instances in urban street scene, blurred edges and serious background interference, a segmentation model RENet based on adaptive regulation convolution and dual-path information embedding was proposed. Firstly, adaptive regulatory convolution was used to replace the original residual structure, and deformable convolution learning space sampling position offset was used to improve the modeling ability of the model for complex image deformation. At the same time, channel mixing was carried out on the multi-branch structure to enhance the information flow between different channels, and the attention mechanism was applied to realize the adaptive calibration of channel weights. Improve the segmentation accuracy of the model for fuzzy and dense objects in complex scenes. Secondly, low dimensional spatial information embedding branches were designed, spatial information excitation and recoding were carried out on different scale feature maps, and low dimensional spatial information was embedded into abstract semantic features to improve the accuracy of model contour segmentation. Finally, a high-level semantic information embedding module is introduced to align the feature map with the semantic box, bridge the semantic and resolution gap between the feature maps, and improve the effectiveness of feature information fusion at different scales. The test results on the self-built data set show that compared with the original YOLACT network model, the average segmentation accuracy of RENet under the complex street background is up to 51.6%, which is 10.4 percentage point higher. At the same time, the network reasoning speed reaches 17.5 frames/s, which verifies the effectiveness of the model optimization and the practicability of the engineering value.