[Purposes]An improved YOLOv4 detection model is proposed to solve the problems of missing detection and false detection when the existing models detect objects with small size and low contrast with the background.[Methods]In order to solve the problem of small size,first,the DDS unit is designed to replace the Res unit in the backbone network.By connecting features of differ-ent levels across layers,complete and rich multi-scale features can be obtained,and small-size dam-age detection can be completed.Second,the gradient harmonized mechanism is introduced into the classification loss function,and the weight of small-size damage is dynamically adjusted to make it fully trained.Aiming at the low contrast between damage and background,first,the coordinate atten-tion mechanism is embedded in the deep network layer of the backbone network to enhance the model's attention to damage characteristics and reduce the interference of background noise.Second,the accurate decoupled head is designed to improve detection accuracy by solving the contradiction be-tween classification and location requirements for features.[Findings]Experimental results demon-strate that the mean average precision of this model is increased by 3.92%compared with that of YO-LOv4,and the detection accuracy of small-size crack damage and low-contrast wear damage is im-proved by 4.32%and 4.24%,respectively,which effectively solves the problems of missed detection and false detection.
关键词
传送带破损/YOLOv4/DDS/unit/梯度协调机制/坐标注意力机制/精确解耦头
Key words
damage of conveyor belt/YOLOv4/DDS unit/gradient harmonized mechanism/co-ordinate attention mechanism/accurate decoupled head
维普
万方数据