RSIn-Dataset: An UAV-Based Insulator Detection Aerial Images Dataset and Benchmark

Shuang, Feng; Han, Sheng; Li, Yong; Lu, Tongwei

doi:10.3390/drones7020125

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…In recent years, with the continuous development of computer vision technology and robotics, researchers have begun to use emerging technologies for power line inspection work, such as image recognition based on deep learning, automatic control of UAVs, and so on. Shuang et al [182] constructed a specialized dataset called RSIn-Dataset to address the lack of power line inspection datasets. This dataset contains four different sizes of insulator targets, totaling 3286.…”

Section: A Urban Infrastructure (Ui) Monitoringmentioning

confidence: 99%

A Survey on UAV Applications in Smart City Management: Challenges, Advances, and Opportunities

Xu,

Wang,

Han

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Smart management of urban space is an important way to achieve the goal of sustainable urban development. Unmanned aerial vehicles (UAVs) have been widely used in urban space management work and have greatly improved the level of it. UAV technology assisting urban space management involves many technical details and has generated a number of problems. Therefore, a systematic review of the applications of UAVs in urban spatial management work is very necessary and can provide a comprehensive reference for relevant researchers. This is conducive to the generation of more new insights, methods, and applications. However, according to our research, there is a lack of relevant systematic investigation. We screened and researched a large number of relevant literatures (about 230) with the help of search tools such as Web of Science and IEEE Explore, and combined with our own working experience to review and summarize the field in an all-round way. Taking the definition, needs, and challenges of urban spatial management as an entry point, this paper systematically summarizes the task flow, working paradigm, technical system, and application direction of UAV-assisted urban spatial management. It also takes our recently developed UAV urban management system as an example to provide an introduction on how to integrate advanced technologies such as UAV and artificial intelligence. Finally, this paper summarizes several concluding findings and proposes several future research directions. The research in this paper shows that UAV technology has already played a great role in urban spatial management work, but it is still far from realizing automation and intelligence, and it needs to continue to make efforts in terms of methods, systems, applications, and policies. In order to achieve these goals, UAV technology can be further integrated with advanced technologies such as deep learning, more types of UAV industry applications can be carried out, more functional UAS can be developed, and better management policies can be formulated.

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Section: A Urban Infrastructure (Ui) Monitoringmentioning

confidence: 99%

A Survey on UAV Applications in Smart City Management: Challenges, Advances, and Opportunities

Xu,

Wang,

Han

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Insulators, as important components of high-voltage transmission lines, serve the functions of electrical separation and support for conductors [ 1 ]. Due to their long-term outdoor exposure to sunlight, rain, climate changes, and chemical corrosion, insulators often suffer from self-exploding defects, causing the disconnection of insulator strings and interfering with their performance, thus affecting the safety and stability of power systems [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Lightweight Detection Methods for Insulator Self-Explosion Defects

Chen,

Deng,

Sun

et al. 2024

Sensors

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The accurate and efficient detection of defective insulators is an essential prerequisite for ensuring the safety of the power grid in the new generation of intelligent electrical system inspections. Currently, traditional object detection algorithms for detecting defective insulators in images face issues such as excessive parameter size, low accuracy, and slow detection speed. To address the aforementioned issues, this article proposes an insulator defect detection model based on the lightweight Faster R-CNN (Faster Region-based Convolutional Network) model (Faster R-CNN-tiny). First, the Faster R-CNN model’s backbone network is turned into a lightweight version of it by substituting EfficientNet for ResNet (Residual Network), greatly decreasing the model parameters while increasing its detection accuracy. The second step is to employ a feature pyramid to build feature maps with various resolutions for feature fusion, which enables the detection of objects at various scales. In addition, replacing ordinary convolutions in the network model with more efficient depth-wise separable convolutions increases detection speed while slightly reducing network detection accuracy. Transfer learning is introduced, and a training method involving freezing and unfreezing the model is employed to enhance the network’s ability to detect small target defects. The proposed model is validated using the insulator self-exploding defect dataset. The experimental results show that Faster R-CNN-tiny significantly outperforms the Faster R-CNN (ResNet) model in terms of mean average precision (mAP), frames per second (FPS), and number of parameters.

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“…By introducing the GSConv module and employing the lightweight CARAFE structure in the neck network, the accuracy of detecting small targets was improved. Shuang et al [ 32 ] proposed an enhanced network, YOLOv4++, based on the improvements made to YOLOv4. The backbone network utilizes MobileNetv1 with depthwise separable convolution, and the model’s effectiveness is further enhanced through improvements in the loss functions.…”

Section: Introductionmentioning

confidence: 99%

Insulator Defect Detection Based on ML-YOLOv5 Algorithm

Wang,

Zhai,

et al. 2023

Sensors

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To address the challenges of balancing accuracy and speed, as well as the parameters and FLOPs in current insulator defect detection, we propose an enhanced insulator defect detection algorithm, ML-YOLOv5, based on the YOLOv5 network. The backbone module incorporates depthwise separable convolution, and the feature fusion C3 module is replaced with the improved C2f_DG module. Furthermore, we enhance the feature pyramid network (MFPN) and employ knowledge distillation using YOLOv5m as the teacher model. Experimental results demonstrate that this approach achieved a 46.9% reduction in parameter count and a 43.0% reduction in FLOPs, while maintaining an FPS of 63.6. It exhibited good accuracy and detection speed on both the CPLID and IDID datasets, making it suitable for real-time inspection of high-altitude insulator defects.

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RSIn-Dataset: An UAV-Based Insulator Detection Aerial Images Dataset and Benchmark

Cited by 8 publications

References 28 publications

A Survey on UAV Applications in Smart City Management: Challenges, Advances, and Opportunities

A Survey on UAV Applications in Smart City Management: Challenges, Advances, and Opportunities

Lightweight Detection Methods for Insulator Self-Explosion Defects

Insulator Defect Detection Based on ML-YOLOv5 Algorithm

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