Grid Foreign Matter Detection Algorithm Based on Improved YOLOv5s
Aiming at the problems of foreign object scale change,low real-time performance and insufficient recognition accuracy in complex environment existed in the detection of foreign matter in power grid and along the transmission line,a power grid foreign body detection algorithm based on improved YOLOv5s framework is proposed.ECA attention mechanism is embedded in the backbone network to reduce background interference,SPD-Conv module is used to replace the convolution module of backbone network.Improved BiFPN is introduced to enhance the detection ability of the model for different size targets.Finally,the Alpha_GIoU loss function is used to replace the CIoU part in the original YOLOv5s.The experimental results show that the mAP value of improved YOLOv5s on the grid foreign object detection dataset reaches 96.98%,achieves the grid foreign matter detection demands under complex environment.
NETL
NSTL
万方数据
