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.

A Lightweight Modified YOLOv5 Network Using a Swin Transformer for Transmission-Line Foreign Object Detection

“…Zhou et al [15] replaced Resnet [17] and FPN in Cascade RCNN [16] with a deeply aggregated feature-extraction network and an efficient weighted, bi-directional featurefusion network to achieve the detection of anti-vibration hammer defects on transmission lines and reduce the computational cost of the model. Jun et al [18] proposed a YOLOV5 algorithm based on the PWR-YOLOV5 corrosion component detection method.…”

“…A higher precision value corresponds to a reduced level of noise in the retrieved outcomes, signifying superior precision. Recall, as expressed in Equation (15), measures the proportion of pertinent instances retrieved from the ground truth, elucidating the model's coverage. Elevated recall values signify the model's enhanced ability to retrieve relevant instances and, consequently, its heightened search comprehensiveness.…”

“…Previous studies have shown that it is possible to accomplish the detection of foreign objects on transmission lines via deep learning techniques [15,16]. However, the existing algorithms suffer from high levels of memory consumption and slow detection speeds, and they are easily affected by background elements.…”

SOD-YOLO: A High-Precision Detection of Small Targets on High-Voltage Transmission Lines

“…Secondly, to enhance the extraction of geometric features from the vibrationproof hammer, monochrome-backgrounded artificial samples were employed during the training stage. Zhou et al [12] proposed a deep aggregation feature extraction network and an efficient weighted feature-fusion network to replace the original ResNet and Feature Pyramid Network (FPN) of Cascade R-CNN, which balances the inference speed and average accuracy during detection; while the two-stage target detection method offers improved accuracy, its detection speed is sluggish, failing to fulfill the real-time demands of UAV detection.…”

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

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”