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OA

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.