Abstract: Aiming at the real-time recognition problem of electrolytic cell transfer robot in electrolytic aluminum workshop,there is a problem that the size difference of the recognition object is too large for the target detection of electrolytic cell equipment and aluminum ingot samples.Generally,the parameters of the target detection algorithm are large,and it is difficult to meet the requirements of real-time detection when deployed on electrolyzer transfer robots.Therefore,a lightweight multi-scale YOLOv5 network model that solves the excessive difference in target size is proposed,and the backbone feature extraction network is replaced by a lightweight Shufflenet V2 network.Add SE attention mechanism to improve the accuracy of small target recognition.In the enhanced feature extraction network,a shallow detection layer is added as the detection layer for smaller targets to achieve the recognition accuracy of multi-scale and large size changes.Experimental results show that the average detection accuracy of the improved YOLOv5 algorithm in the electrolyzer equipment and sample identification of the electrolyzer transfer robot is 93.5%,which is 1.5% higher than the average detection accuracy of the YOLOv5 algorithm,the number of model parameters reduces by about 39.4%,and the average detection speed of each picture increases by 2.5 milliseconds,which is conducive to deployment to the Electrolyzer transfer robot.

Key words: YOLOv5, Lightweight, Attention mechanism, Multi-scale object detection, Target size difference