An Union Method Combining the Stitching of Normal Images and the Unsupervised Semantic Segmentation of Stitched Image

The union combining the stitching of normal images and unsupervised semantic segmentation of the stitched image is an important region, which is crucial for autonomous driving, intelligent robots, and vehicle detection. This paper designs an union method combining the stitching of normal images and unsupervised semantic segmentation of the stitched image. The normal images are stitched by using the image stitching method designed by Ribeiro D.. The semantic segmentation method for the stitched image uses the method opened in the github. The stitched image contains image distortion. The distortion of the stitched image will make the feature extraction unreasonable. The distortion form of the stitched image is different from the distortion form of the panoramic image combined by line images. Therefore, the DCM proposed by Xing Hu is useless to extract features of the stitched image reasonable. This paper improves the DCM as the improved distortion convolution module (IDCM) by using the deformable convolution, the clamp module, the type transformation module, and the gather module. The IDCM is added before the unsupervised semantic segmentation method opened in the github to extract features reasonable. The IDCM-NUSSM method and the ISM-IDCM-NUSSM method are proposed. The experimental results show the better performance of the designed methods.