Protective Panel Detection Model Based on Improved YOLOv5s
In view of the characteristics of high similarity and dense distribution of internal devices in power protective panel,this paper proposes a detection model for protective panel.called YOLOv5s-PBC.The model is based on YOLOv5s and uses a lightweight network called PP-LCNet as the backbone feature extraction network to reduce network parameters and improve detection speed.It uses the weighted bi-directional feature pyramid network(BiFPN)structure as the basic unit for feature fusion to enhance the model's perception ability.The CARAFE upsampling operator is introduced to overcome the problem of information loss during upsampling.The experimental results demonstrate that the model has good recognition performance,with a mean average precision(mAP)of 92.6％,a frame rate of 50.25 fps,and a model size of only 17 MB.It is suitable for deployment on mobile embedded devices and has practical value.
detection of power protective paneldeep learningYOLOv5s
万方数据
维普
