Aiming at the problem that the existing detection model cannot accurately detect electric vehi-cles in elevators in real-time scenarios,an improved YOLOv5s model is proposed to detect electric vehi-cles in elevator scenarios.The improved model integrates the global attention mechanism into the neck feature fusion network of YOLOv5s model to enhance the model's learning ability of fused features;the fully decoupled detection head is used to replace the coupling head of the original YOLOv5s model,and the target in the detection process is the two subtasks of classification and bounding box regression are decoupled to improve the accuracy of model detection.Experimental results show that on the self-built e-lectric vehicles image data set E-Car in the elevator,the improved YOLOv5s model significantly im-proves the detection effect,and the detection indicators P,R,mAP_0.5 and mAP_0.5:0.95 reach 94. 5％,92.2％,96.9％ and 64.5％,are 4.3％,2.8％,3.8％ and 4.6％ higher than the original model re-spectively.Compared with other mainstream target detection models,the improved YOLOv5s model has a higher detection accuracy while maintaining a certain detection speed,and can realize efficient electric vehicles detection in elevators.
关键词
电梯内电瓶车检测/YOLOv5s/全局注意力/充分解耦头
Key words
detection of electric vehicles in elevator/YOLOv5s/global attention/fully decoupled head
维普
万方数据