Defect Detection Scheme for Key Equipment of Transmission Line for Complex Environment

Abstract

Aiming at the difficulty in detecting defects of key equipment of transmission lines in small samples and complex environments, and the problems of low accuracy and unreliability in one-time detection using traditional deep learning-based methods, an image detection scheme combining optimized deep convolutional neural networks and Kalman filtering is proposed. The convolutional neural network architecture is based on Faster Region-based Convolutional Neural Networks (R-CNNs). First, the model backbone network is constructed by MobileNet, which effectively reduces the computational cost. Secondly, a soft nonmaximum suppression algorithm is integrated to solve the occlusion problem of target parts, and the context-aware ROI pooling layer replaces the original pooling layer, maintaining the original structure of small-sized components. Finally, the detection results are corrected twice by Kalman filtering to further improve the detection accuracy and reliability. The experimental results show that this method can realize the accurate detection of components in complex transmission line equipment, the mean Average Precision (mAP) reaches 91.10%, which is 11.05% higher than the original model, and the detection time of each picture is only 0.05 s. Compared with other detection algorithms under the same conditions, the comprehensive performance of the proposed method can be improved by 20%.

EDF-YOLOv5: An Improved Algorithm for Power Transmission Line Defect Detection Based on YOLOv5

“…This adjustment aims to better focus the sampling points on the target features. The output for any pixel of the input feature map is expressed as per Equation (7). DCNv3 not only exhibits enhanced geometric transformation capabilities but also, through the application of grouped convolution, segregates the spatial aggregation process into G groups.…”

“…To prevent faults, the initial approach involved manual patrolling to inspect power transmission line defects. Nevertheless, these conventional manual patrolling methods are not only time-consuming and labor-intensive but also introduce specific risks to the patrolling personnel [6,7].…”