RSIn-Dataset: An UAV-Based Insulator Detection Aerial Images Dataset and Benchmark
Power line inspection is an important part of the smart grid. Efficient real-time detection of power devices on the power line is a challenging problem for power line inspection. In recent years, deep learning methods have achieved remarkable results in image classification and object detection. How...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/7/2/125 |
| _version_ | 1797621438258085888 |
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| author | Feng Shuang Sheng Han Yong Li Tongwei Lu |
| author_facet | Feng Shuang Sheng Han Yong Li Tongwei Lu |
| author_sort | Feng Shuang |
| collection | DOAJ |
| description | Power line inspection is an important part of the smart grid. Efficient real-time detection of power devices on the power line is a challenging problem for power line inspection. In recent years, deep learning methods have achieved remarkable results in image classification and object detection. However, in the power line inspection based on computer vision, datasets have a significant impact on deep learning. The lack of public high-quality power scene data hinders the application of deep learning. To address this problem, we built a dataset for power line inspection scenes, named RSIn-Dataset. RSIn-Dataset contains 4 categories and 1887 images, with abundant backgrounds. Then, we used mainstream object detection methods to build a benchmark, providing reference for insulator detection. In addition, to address the problem of detection inefficiency caused by large model parameters, an improved YoloV4 is proposed, named YoloV4++. It uses a lightweight network, i.e., MobileNetv1, as the backbone, and employs the depthwise separable convolution to replace the standard convolution. Meanwhile, the focal loss is implemented in the loss function to solve the impact of sample imbalance. The experimental results show the effectiveness of YoloV4++. The mAP and FPS can reach 94.24% and 53.82 FPS, respectively. |
| first_indexed | 2024-03-11T08:55:56Z |
| format | Article |
| id | doaj.art-72a80f9f691c49638ea0b14bb6902724 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-11T08:55:56Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-72a80f9f691c49638ea0b14bb69027242023-11-16T20:06:58ZengMDPI AGDrones2504-446X2023-02-017212510.3390/drones7020125RSIn-Dataset: An UAV-Based Insulator Detection Aerial Images Dataset and BenchmarkFeng Shuang0Sheng Han1Yong Li2Tongwei Lu3Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment, School of Electrical Engineering, Guangxi University, Nanning 530004, ChinaGuangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment, School of Electrical Engineering, Guangxi University, Nanning 530004, ChinaGuangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment, School of Electrical Engineering, Guangxi University, Nanning 530004, ChinaGuangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment, School of Electrical Engineering, Guangxi University, Nanning 530004, ChinaPower line inspection is an important part of the smart grid. Efficient real-time detection of power devices on the power line is a challenging problem for power line inspection. In recent years, deep learning methods have achieved remarkable results in image classification and object detection. However, in the power line inspection based on computer vision, datasets have a significant impact on deep learning. The lack of public high-quality power scene data hinders the application of deep learning. To address this problem, we built a dataset for power line inspection scenes, named RSIn-Dataset. RSIn-Dataset contains 4 categories and 1887 images, with abundant backgrounds. Then, we used mainstream object detection methods to build a benchmark, providing reference for insulator detection. In addition, to address the problem of detection inefficiency caused by large model parameters, an improved YoloV4 is proposed, named YoloV4++. It uses a lightweight network, i.e., MobileNetv1, as the backbone, and employs the depthwise separable convolution to replace the standard convolution. Meanwhile, the focal loss is implemented in the loss function to solve the impact of sample imbalance. The experimental results show the effectiveness of YoloV4++. The mAP and FPS can reach 94.24% and 53.82 FPS, respectively.https://www.mdpi.com/2504-446X/7/2/125power line inspectioninsulator detectiondeep learningconvolutional neural network |
| spellingShingle | Feng Shuang Sheng Han Yong Li Tongwei Lu RSIn-Dataset: An UAV-Based Insulator Detection Aerial Images Dataset and Benchmark Drones power line inspection insulator detection deep learning convolutional neural network |
| title | RSIn-Dataset: An UAV-Based Insulator Detection Aerial Images Dataset and Benchmark |
| title_full | RSIn-Dataset: An UAV-Based Insulator Detection Aerial Images Dataset and Benchmark |
| title_fullStr | RSIn-Dataset: An UAV-Based Insulator Detection Aerial Images Dataset and Benchmark |
| title_full_unstemmed | RSIn-Dataset: An UAV-Based Insulator Detection Aerial Images Dataset and Benchmark |
| title_short | RSIn-Dataset: An UAV-Based Insulator Detection Aerial Images Dataset and Benchmark |
| title_sort | rsin dataset an uav based insulator detection aerial images dataset and benchmark |
| topic | power line inspection insulator detection deep learning convolutional neural network |
| url | https://www.mdpi.com/2504-446X/7/2/125 |
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