A Drone Based Transmission Line Components Inspection System with Deep Learning Technique

Siddiqui, Zahid Ali; Park, Unsang

doi:10.3390/en13133348

Cited by 50 publications

(27 citation statements)

References 44 publications

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“…In addition, large-scale public data sets and high-performance hardware processing systems have elevated object detection algorithms based on deep learning to new levels, which can be divided into one-and two-stage detectors. Specifically, one-stage detectors, as represented by the single-shot multibox detector (SSD) [7] and you only look once (YOLO) [8], have been used to detect antivibration hammers in transmission lines [9,10]. In reference [10], the accuracies of antivibration hammer detection algorithms based on the SSD are significantly higher than machine learning-based methods used in reference [6].…”

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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“…If there is a difference in hot spot temperature for the same component, then there is a fault that must be repaired or replaced. We used a method for analysing thermal images including convert colour images to grey images and apply k-means clustering unsupervised machine learning algorithm with k=18 to segment different parts of an image using OpenCV in python, with otsu-threshold and dilation operation mask (8,8) to highlight the hot spot region [14]. Fig 2 shows the algorithm steps where we can see two insulators in the infrared image, one defective and the other normal.…”

Section: Thermal Cameramentioning

confidence: 99%

“…The projection is analysed utilizing the facilitates and distinguishing proof focuses for recognizing the object [7]. Siddiqui, Zahid Ali park, unsung in [8] proposed method to detect the fault of splits and puncture in polymer insulator by using ellipse detection algorithm and then apply the splits algorithm on individual caps, also proposed method to detect the broken or missing ceramic disk by colour clustering and morphological operations. In their research paper [9] they developed and implemented powerful algorithms for artificial vision, distance calculation, and hot spot detection [9].this paper [10] talks about the utilize of multi-agent frameworks as a reasonable arrangement to address this challenge by analysing their benefits when applying them to the field of savvy lattices and looking over existing works and activities.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Controller Design of the Vehicle Sonar Tracking Checker Fault and Repair of High Voltage Transmission Line

Hani¹,

Gizi²

2022

Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies, IMDC-IST 2021, 7-9

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Inspection of high-voltage transmission lines is a dangerous and timeconsuming activity that requires specially trained technicians to work tens of meters above the ground and near-live lines carrying thousands of volts. This paper presents the development of a teleoperated Intelligent Controller Vehicle Sonar Tracking Checker designed by use UAV in the collection of data power lines for preventive maintenance of high-voltage lines. The Vehicle Sonar Tracking Checker was designed with mobility in mind, so those cable spacers, suspension clamps, and other obstacles, which so far have prevented the inspection of high-voltage lines, will not be prevented the inspection of the lines. The safe operation of the transmission line and its components, on the other hand, necessitates periodical inspections to detect corrosion and other climatic and mechanical problems and flaws. The necessity for rapid and robust algorithms for the interpretation of photos or video obtained by drones during inspections has increased as the usage of unmanned autonomous vehicles (UAVs) for ecological observation grows. use UAV in the collection of power lines data and for analysis defects, various algorithms are used from image processing with MATLAB and python.

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“…According to statistics, accidents caused by insulator faults account for the largest percent of the total power grid accidents [3]. Therefore, automatic and timely detection of insulator faults in high-voltage transmission lines has important practical significance [4,5]. In the last decade, with the development of computer vision and image processing technology, the traditional manual patrol has gradually been replaced by unmanned aerial vehicle (UAV) inspection [6][7][8], and aerial images captured by UAV have been widely used for high-voltage transmission lines off-line inspection.…”

Section: Introductionmentioning

confidence: 99%

An Improved Method Based on Deep Learning for Insulator Fault Detection in Diverse Aerial Images

Liu

et al. 2021

Energies

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Insulators play a significant role in high-voltage transmission lines, and detecting insulator faults timely and accurately is important for the safe and stable operation of power grids. Since insulator faults are extremely small and the backgrounds of aerial images are complex, insulator fault detection is a challenging task for automatically inspecting transmission lines. In this paper, a method based on deep learning is proposed for insulator fault detection in diverse aerial images. Firstly, to provide sufficient insulator fault images for training, a novel insulator fault dataset named “InSF-detection” is constructed. Secondly, an improved YOLOv3 model is proposed to reuse features and prevent feature loss. To improve the accuracy of insulator fault detection, SPP-networks and a multi-scale prediction network are employed for the improved YOLOv3 model. Finally, the improved YOLOv3 model and the compared models are trained and tested on the “InSF-detection”. The average precision (AP) of the improved YOLOv3 model is superior to YOLOv3 and YOLOv3-dense models, and just a little (1.2%) lower than that of CSPD-YOLO model; more importantly, the memory usage of the improved YOLOv3 model is 225 MB, which is the smallest between the four compared models. The experimental results and analysis validate that the improved YOLOv3 model achieves good performance for insulator fault detection in aerial images with diverse backgrounds.

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A Drone Based Transmission Line Components Inspection System with Deep Learning Technique

Cited by 50 publications

References 44 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

Intelligent Controller Design of the Vehicle Sonar Tracking Checker Fault and Repair of High Voltage Transmission Line

An Improved Method Based on Deep Learning for Insulator Fault Detection in Diverse Aerial Images

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