Improved Faster R-CNN algorithm and its application in power work safety wearing detection
The correct safety wearing of operation and maintenance personnel is an important measure to ensure the safety of electric power operations.The computer vision deep learning algorithm provides a new method for the supervision of safety wearing of electric power operations.Faster R-CNN(faster region-based convolutional neural network)is an effective object detection method,which is improved in this paper.Firstly,the lightweight convolutional neural network EfficientNetV2(efficient network version 2)was used as the backbone of Faster R-CNN to improve the detection accuracy and speed of the algorithm in a balance manner.Then,CBAM(convolutional block attention module)was introduced before RPN(region proposal network)to further improve the detection accuracy.The method was tested in combination with the actual electric power operations safety wearing detection scenarios.The results showed that compared with current Faster R-CNN algorithm with VGG-16(visual geometry group-16)and ResNet-50(residual network-50)as the backbone,the detection accuracy and speed of the improved Faster R-CNN algorithm were improved,with the mean average precision(mAP)reaching 85.46％and the detection speed reaching 47.82 frames per second(FPS).The application capability of Faster R-CNN in electric power operation safety wearing detection is improved to a certain extent.
safe wearing of electric power operationsFaster R-CNNEfficientNetV2CBAM
NETL
NSTL
万方数据
