Complex Traffic Scene Image Classification Based on Sparse Optimization Boundary Semantics Deep Learning

Abstract

With the rapid development of intelligent traffic information monitoring technology, accurate identification of vehicles, pedestrians and other objects on the road has become particularly important. Therefore, in order to improve the recognition and classification accuracy of image objects in complex traffic scenes, this paper proposes a segmentation method of semantic redefine segmentation using image boundary region. First, we use the SegNet semantic segmentation model to obtain the rough classification features of the vehicle road object, then use the simple linear iterative clustering (SLIC) algorithm to obtain the over segmented area of the image, which can determine the classification of each pixel in each super pixel area, and then optimize the target segmentation of the boundary and small areas in the vehicle road image. Finally, the edge recovery ability of condition random field (CRF) is used to refine the image boundary. The experimental results show that compared with FCN-8s and SegNet, the pixel accuracy of the proposed algorithm in this paper improves by 2.33% and 0.57%, respectively. And compared with Unet, the algorithm in this paper performs better when dealing with multi-target segmentation.