Abstract
As the UAV(Unmanned Aerial Vehicle) carrying target detection algorithm in transmission line insulator inspection, we propose a lightweight YOLOv7 insulator defect detection algorithm for the problems of inferior insulator defect detection speed and high model complexity. Firstly, a lightweight DSC-SE module is designed using a DSC(Depthwise Separable Convolution) fused SE channel attention mechanism to substitute the SC(Standard Convolution) of the YOLOv7 backbone extraction network to decrease the number of parameters in the network as well as to strengthen the shallow network’s ability to obtain information about target features. Then, in the feature fusion part, GSConv(Grid Sensitive Convolution) is used instead of standard convolution to further lessen the number of parameters and the computational effort of the network. EIoU-loss(Efficient-IoU) is performed in the prediction head part to make the model converge faster. According to the experimental results, the recognition accuracy rate of the improved model is 95.2%, with a model size of 7.9M. Compared with YOLOv7, the GFLOPs are reduced by 54.5%, the model size is compressed by 37.8%, and the accuracy is improved by 4.9%. The single image detection time on the Jetson Nano is 105ms and the capture rate is 13FPS. With guaranteed accuracy and detection speed, it meets the demands of real-time detection.
Funder
scientific research project of Jilin Provincial Science and Technology Program
Publisher
Public Library of Science (PLoS)
Reference36 articles.
1. Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network;J Chen;Ieee Transactions on Instrumentation and Measurement,2018
2. Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks;X Tao;Ieee Transactions on Systems Man Cybernetics-Systems,2020
3. Aerial image recognition of transmission line insulator strings based on color model and texture features;T Bo;Journal of Electric Power Science and Technology,2020
4. Catenary insulator defect detection based on contour features and gray similarity matching;P Tan;Journal of Zhejiang University-SCIENCE A,2020
5. Nonlinear mechanical model of composite insulator interface and nondestructive testing method for weak bonding defects;H Wang;Proceedings of the CSEE,2019
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