Autonomous Vision-Based Primary Distribution Systems Porcelain Insulators Inspection Using UAVs

Rahman, Ehab Ur; Zhang, Yihong; Ahmad, Sohail; Ahmad, Hafiz Ishfaq; Jobaer, Sayed

doi:10.3390/s21030974

Cited by 54 publications

(26 citation statements)

References 34 publications

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“…The true positive (TP), false positive (FP), true negative (TN), and false negative (FN) used in binary classification problem are defined as shown in Table 3. Precision (P), which denotes the proportion of all correctly detected results to all the predicted results, is defined as Formula (7). Recall (R), which denotes the proportion of all correctly detected results to all the results that should be predicted, is defined as Formula (8).…”

Section: Quantitative and Qualitative Analysismentioning

confidence: 99%

“…Specifically, the AP values of the four models were YOLOv3 (92.8%), YOLOv3-dense (94.1%), CSPD-YOLO (97.7%), and the improved YOLOv3 (96.5%); the Precision (P) values of the four models were YOLOv3 (94%), YOLOv3-dense (95%), CSPD-YOLO (99%), and the improved YOLOv3 (98%); the Recall (R) values of the four models were YOLOv3 (91%), YOLOv3-dense (91%), CSPD-YOLO (97%), and the improved YOLOv3 (95%), respectively, and the corresponding histogram is shown in Figure 9. Precision (P), which denotes the proportion of all correctly detected results to all the predicted results, is defined as Formula (7). Recall (R), which denotes the proportion of all correctly detected results to all the results that should be predicted, is defined as Formula (8).…”

Section: Quantitative and Qualitative Analysismentioning

confidence: 99%

“…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with the development of computer vision and image processing technology, the traditional manual patrol has gradually been replaced by unmanned aerial vehicle (UAV) inspection [6][7][8], and aerial images captured by UAV have been widely used for high-voltage transmission lines off-line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. Insulators and their fault images used for detection are shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Quantitative and Qualitative Analysismentioning

confidence: 99%

Section: Quantitative and Qualitative Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Liu

et al. 2021

Energies

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“…Процес локалізації такий, що налаштований БПЛА, оснащений системою пошуку та позиціонування, скерований БПЛА для певного маршруту. Оскільки цільовий пристрій періодично передає радіо на певній частоті [29], спрямовані антени, встановлені на БПЛА, можна використовувати для збору RSSI, оцінки AOA сигналу відповідно до RSSI і згодом отримання інформації про напрямок цілі.…”

Section: бпла для порятунку при катастрофахunclassified

Vehicle Routing Problem When Using UAVs

Norba

2021

Cybernetics and Computer Technologies

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Introduction. In recent years, the use of unmanned aerial vehicles (UAVs) is growing rapidly. Initially introduced for military purposes, drones and related technologies have been successfully switched to a number of new civilian applications in the last few years, such as delivery, logistics, surveillance, entertainment, and more. They also opened up new opportunities, such as working in difficult or dangerous areas. The UAV has the potential to solve the problem of air mobility, allowing to change transport and logistics in the future. Combining UAVs with traditional land vehicles can solve the last-mile delivery problem by achieving significant improvements in distribution costs and speed of vehicle delivery. One of the biggest challenges is to plan UAV routes with a number of constraints, including time, distance or energy costs, cargo weight, environmental and environmental conditions (such as wind direction or obstacles), UAV battery life, and demand requirements. users you want to visit. Thus, it revealed the need to classify different types of research and study the general characteristics of the study area. This article aims to help identify the main topics and new areas of research, as well as provides a published overview of the current state and contribution to the problem of UAV routing, as well as a general categorization of the problem of vehicle routing (VRP). The purpose of the paper is to analyze the scientific contributions to the problem of UAV routing to determine the main characteristics of these problems, as well as trends in research and recent improvements. Results. Sources are classified according to the areas of application of UAVs; methods that include exact, heuristic, metaheuristic, and mixed algorithms are mentioned. Conclusions. An overview of the work on routing problems using UAVs and the tasks they generate, trends in research and recent developments. Keywords: Unmanned aerial vehicle, routing, vehicle, optimization.

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“…Because of this, they are more feasible for real-world application [35]. Meanwhile, the networks of YOLOv2 and YOLOv3-which are single-stage models-have been widely used for object detection [36][37][38][39][40][41]. Consequently, existing single-stage models can be adapted to detect insulators by transferring learning strategies.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv3 Network for Insulator Detection in Aerial Images with Diverse Background Interference

Liu

et al. 2021

Electronics

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Automatic inspection of insulators from high-voltage transmission lines is of paramount importance to the safety and reliable operation of the power grid. Due to different size insulators and the complex background of aerial images, it is a difficult task to recognize insulators in aerial views. Most of the traditional image processing methods and machine learning methods cannot achieve sufficient performance for insulator detection when diverse background interference is present. In this study, a deep learning method—based on You Only Look Once (YOLO)—will be proposed, capable of detecting insulators from aerial images with complex backgrounds. Firstly, aerial images with common aerial scenes were collected by Unmanned Aerial Vehicle (UAV), and a novel insulator dataset was constructed. Secondly, to enhance feature reuse and propagation, on the basis of YOLOv3 and Dense-Blocks, the YOLOv3-dense network was utilized for insulator detection. To improve detection accuracy for different sized insulators, a structure of multiscale feature fusion was adapted to the YOLOv3-dense network. To obtain abundant semantic information of upper and lower layers, multilevel feature mapping modules were employed across the YOLOv3-dense network. Finally, the YOLOv3-dense network and compared networks were trained and tested on the testing set. The average precision of YOLOv3-dense, YOLOv3, and YOLOv2 were 94.47%, 90.31%, and 83.43%, respectively. Experimental results and analysis validate the claim that the proposed YOLOv3-dense network achieves good performance in the detection of different size insulators amid diverse background interference.

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Autonomous Vision-Based Primary Distribution Systems Porcelain Insulators Inspection Using UAVs

Cited by 54 publications

References 34 publications

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

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

Vehicle Routing Problem When Using UAVs

Improved YOLOv3 Network for Insulator Detection in Aerial Images with Diverse Background Interference

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