A High-Efficiency Deep-Learning-Based Antivibration Hammer Defect Detection Model for Energy-Efficient Transmission Line Inspection Systems

Abstract

Automated inspection using unmanned aerial vehicles (UAVs) is an essential means to ensure safe operations of the power grid. Defect detection for antivibration hammers on transmission lines in inspection imagery is one of the critical tasks for automated UAV inspection. It needs a machine interpretation system to automatically detect numerous inspection images. In this paper, a high-efficiency model based on Cascade RCNN (region-convolutional neural network) is proposed to detect antivibration hammer defects with reduced costs and speedier response, which applies in energy-efficient transmission line inspection systems. Firstly, to reduce computational costs, this study modifies the Cascade RCNN with a probabilistic interpretation to achieve the best trade-off between the inference time and average precision. Secondly, an antivibration hammer defect detector (AVHDD) model is proposed that uses a deep layer aggregation-based feature extraction network and a highly effective weighted bidirectional feature fusion network to replace the original ResNet and FPN on the modified Cascade RCNN to further enhance the model performance. Finally, a fine classification (FC) scheme for the types of antivibration hammer defects is proposed based on defect features to rationalize the model. The AVHDD reached an experimental mAP of 97.24% when IoU = 0.75, which is 2.93% higher than the original Cascade RCNN, and the defect recall was 98.9% while also significantly improving the inference speed. Moreover, the experimental results indicate that the overall performance of the proposed model is superior to typical models, confirming its suitability for energy-efficient transmission line inspection systems.