An improved Faster R-CNN for defect recognition of key components of transmission line

Ni, Hongxia; Wang, Minzhen; Zhao, Liying

doi:10.3934/mbe.2021237

Cited by 24 publications

(15 citation statements)

References 7 publications

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“…Among them, the antivibration hammer detection algorithm based on the faster RCNN has experienced a research upsurge [13]. Furthermore, reference [14] enhanced the feature extraction capabilities using a more powerful backbone as the feature extraction network of the faster RCNN and preprocessed the input image to reduce the negative impact of image quality inhomogeneity on the detection performance. us, the model not only can detect antivibration hammers but also can identify associated defects.…”

Section: Related Workmentioning

confidence: 99%

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

Zhou

Wen

Qian

et al. 2022

International Journal of Antennas and Propagation

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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.

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Section: Related Workmentioning

confidence: 99%

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

Zhou

Wen

Qian

et al. 2022

International Journal of Antennas and Propagation

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“…This method makes full use of shallow network to detect tiny objects. Using deep neural networks to detect large objects greatly improves the detection efficiency [20]. Because the extracted feature map is incomplete, the semantic loss in the image is caused, and the detection performance is poor.…”

Section: Detection Methods Of Weak Motion In Transmission Linementioning

confidence: 99%

Research on Intelligent Monitoring System of Intelligent Transmission Line Based on Computer Internet of Things Technology

Kaipeng Zhang, Jingxu Zuo

2024

jes

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The author proposes an intelligent early-warning system for preventing external damage of transmission lines. The system uses TI-TMS320DM6446 as the core, the peripheral circuit configuration and video monitoring; The moving objects captured by PTZ camera are detected and identified by using mixed Gaussian model, background difference and minimum tangent matrix, and alarm signals are provided. According to the particularity of the power supply of the equipment on the transmission line, the basic power management of the system is analyzed in detail. A reliable power management scheme is designed. The practical application proves that this method has good real-time performance and has obvious effect on preventing power cable damage by force.

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“…Convolutional neural networks have been widely applied in target recognition [21][22][23] and classification [24][25][26]. The constructed CNN architecture and its parameter settings have been listed in Fig.…”

Section: Convolutional Neural Network Structurementioning

confidence: 99%

Classification of Transmission Line Ground Short Circuit Fault Based on Convolutional Neural Network

Guo¹,

Tang²,

Ao³

et al. 2022

Energy Engineering

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Ground short circuit faults in current transmission lines are common in the power systems. In order to prevent the power system from aggravating the accident caused by short-circuit faults of transmission lines, a novel convolutional neural network (CNN) model is constructed to identify the short-circuit fault of the transmission line in the power system. The CNN model is mainly consisted of five convolutional layers, three max-pooling layers, one concatenate layer, one dropout layer, one fully connected layer, and a Softmax classifier. This method uses a fixed time window to intercept system short-circuit fault data, extracts the deep features of these data from the training samples, and then corresponds the extracted features to labels one-to-one. Finally, in PSCAD/EMTDC, the new England 10 machine 39 nodes are taken as an example to realize the simulation. The experimental results show that the CNN model can quickly and accurately identify the short-circuit fault types, and the optimal model accuracy rate reaches 99.95%. The results of this manuscript -have positive effect on enhancing the disaster prevention capability and the operation stability of transmission lines.

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An improved Faster R-CNN for defect recognition of key components of transmission line

Cited by 24 publications

References 7 publications

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

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

Research on Intelligent Monitoring System of Intelligent Transmission Line Based on Computer Internet of Things Technology

Classification of Transmission Line Ground Short Circuit Fault Based on Convolutional Neural Network

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