2results about How to "Improving Semantic Segmentation Accuracy" patented technology

The application discloses a convex mirror reflection image generation and semantic segmentation method and device, and relates to the field of convex mirror reflection images. The generation method comprises the following steps: a world coordinate system and a camera coordinate system are constructed, and a relationship between the world coordinate system and the camera coordinate system is established based on pose parameters of a convex mirror reflection image; a radial distortion is performed on a planar image placed in the camera coordinate system; and the distorted image is rotated around the X-axis and the Y-axis of the world coordinate system to obtain a convex mirror reflection image of the planar image. The application simulates convex mirror imaging by means of a convex mirror simulation module, and makes the simulated convex mirror image and the real image close in geometric shape through adversarial learning, so that the semantic segmentation precision of the real convex mirror reflection image is improved.

This invention belongs to the field of semantic segmentation technology, proposing a semantic segmentation method and system based on multi-scale semantic enhancement. The method includes: constructing a multi-scale semantic segmentation model; acquiring an input image and inputting it into a backbone network for feature extraction, obtaining a shallow feature map containing spatial structure information and a deep feature map containing high-level semantic information; inputting the deep feature map into a multi-scale semantic enhancement module for multi-scale modeling enhancement, obtaining deep enhanced features; inputting the deep enhanced features into a multi-scale context aggregation module for global semantic integration, obtaining integrated features; inputting the shallow feature map and integrated features into a feature fusion module for feature fusion, obtaining fused features; and inputting the fused features into a classification output module for classification processing and outputting pixel-level semantic segmentation results. This invention solves the problems of insufficient multi-scale modeling capability, inadequate representation of structural information, and difficulty in balancing accuracy and efficiency in existing semantic segmentation methods.