A Semantic Segmentation Algorithm for Street View Images Based on Deformable Convolution Technique
The current image semantic segmentation algorithms may have the defects of discontinuity of segmented images and loss of fine-scale targets,so we proposed a deformable convolutional fusion enhanced image semantic seg-mentation algorithm.The algorithm integrates the HRNet network framework,Xception Module and deformable convo-lution,optimizes the existing Bottleneck module of HRNet with lightweight Xception Module,and fuses deformable convolution in the first stage of the network to enhance the recognition accuracy of fine-scale target features by build-ing a lightweight fusion enhancement network.This lightweight fusion-enhanced network obtains relatively more se-mantic feature information of the fine-scale target when the coarse-scale target is segmented,which further alleviates the discontinuity of the semantic segmented image and the loss of the fine-scale target.Using the Cityscapes dataset,the experimental results can illustrate that the optimized algorithm has significantly enhanced the segmentation accura-cy for fine-scale targets,while solving the problem of segmentation discontinuity caused by semantic segmentation of images.The experiments were conducted using the publicly available dataset PASCAL VOC 2012,which further vali-dates the robustness and generalization ability of the optimized algorithm.
NETL
NSTL
万方数据
