中山大学学报（自然科学版）（中英文）2026，Vol.65Issue(1)：13-22,10.DOI:10.13471/j.cnki.acta.snus.ZR20240344
基于轻量化与注意力机制的船舶除漆机器人实时目标检测
Ship paint-removal robots real-time object detection based on lightweight and attention mechanism
摘要
Abstract
When the automatic ship paint-removal robot encounters external interference,existing algorithms suffering from performance degradation and insufficient real-time processing capability.To address these challenges,the Repvit-MobileNet block is integrated into the backbone network of YOLOV5 to enhance detection speed.Additionally,the positional attention mechanism has been incorporated after each stage of the backbone network,broadening the model's global receptive field and improving both target localization and interference resistance.Then,a convolutional block attention module(CBAM)is implemented in the neck network,and the feature extraction ability is enhanced by integrating the CBMA module to improve the detection performance of the network model.Lastly,a Refine-Loss loss function is proposed to optimize the geometric relationship between the predicted bounding box and the true bounding box which also balances weight and confidence information related to IOU,leading to improved accuracy in detecting the robot's target position.Subsequent experiments from ship robotic datasets show that the lightweight YOLOV5 network combining Repvit-MobileNet block and attention mechanism can reach 84.1%in the experiment with average precision,and the inference speed on the edge device reaches 26.6 f/s,which meets the need of industrial applications for object detection of ship paint-removal robots.关键词
除漆机器人/轻量化/注意力机制/目标检测Key words
paint-removal robot/light weight/attention mechanism/object detection分类
信息技术与安全科学引用本文复制引用
袁小芳,李潘,孙荣武,许浩志..基于轻量化与注意力机制的船舶除漆机器人实时目标检测[J].中山大学学报（自然科学版）（中英文）,2026,65(1):13-22,10.基金项目
国家自然科学基金(62473140) （62473140）
