Abstract
This study develops a YOLO (You Only Look Once)-based 3D perception algorithm for UVMS (Underwater Vehicle-Manipulator Systems) for precise object detection and localization, crucial for enhanced grasping tasks. The object detection algorithm, YOLOv5s-CS, integrates an enhanced YOLOv5s model with C3SE attention and SPPFCSPC feature fusion, optimized for precise detection and two-dimensional localization in underwater environments with sparse features. Distance measurement is further improved by refining the SGBM (Semi-Global Block Matching) algorithm with Census transform and subpixel interpolation. Ablation studies highlight the YOLOv5s-CS model’s enhanced performance, with a 3.5% increase in mAP and a 6.4% rise in F1 score over the base YOLOv5s, and a 2.1% mAP improvement with 15% faster execution than YOLOv8s. Implemented on a UVMS, the algorithm successfully conducted pool grasping experiments, proving its applicability for autonomous underwater robotics.
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