Detection Method of Water-Surface Obstacles for Unmanned Ships Based on Panoramic Vision
During the navigation of an unmanned ship,an inadequate perspective may cause obstacles on the water-surface to be missed.To enable the safe and normal operation of unmanned ships,this study proposes a panoramic vision-based method for the detection of water-surface obstacles.Panoramic vision is employed because it has the advantage of horizontal large-field monitoring,in contrast to traditional monocular and binocular vision.In the proposed approach,a multi-camera panoramic vision system acquires an image to be stitched.The Speeded-Up Robust Feature(SURF)algorithm then performs image registration.A k-dimensional tree constructs a data index,facilitating search-space level classification and fast matching.The M-estimation-based sample consensus algorithm optimizes the matching points and eliminates the mismatched points.A specifically designed arc function-based weighted fusion algorithm stitches the gaps and overcomes ghosting in the overlapping areas during image fusion.Finally,this study proposes an improved water-surface obstacle target detection model DS-YOLOv5s.This model takes the stitched panoramic image as input to detect target obstacles.In experiment,the improved SURF algorithm improved the feature-point matching accuracy by 11.47 percentage points compared to the SURF algorithm,and shorten the matching time by 5.83 seconds compared to the SURF,RANSAC algorithm.The DS-YOLOv5s model mAP@0.5 reached 95.7%,with a detection speed of 51 frames/s,conforming to real-time object detection standards.
国家科技期刊平台
NSTL
万方数据
维普
