A novel arc detection and identification method in pantograph-catenary system based on deep learning
Abstract Arc detection is crucial for ensuring the safe operation of power systems, where timely and accurate detection of arcs can prevent potential hazards such as fires, equipment damage, or system failures. Traditional arc detection methods, while functional, often suffer from low detection accu...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88109-x |
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| author | Yue Yan Hu Liu Linfeng Gan Runtong Zhu |
| author_facet | Yue Yan Hu Liu Linfeng Gan Runtong Zhu |
| author_sort | Yue Yan |
| collection | DOAJ |
| description | Abstract Arc detection is crucial for ensuring the safe operation of power systems, where timely and accurate detection of arcs can prevent potential hazards such as fires, equipment damage, or system failures. Traditional arc detection methods, while functional, often suffer from low detection accuracy and high computational complexity, especially in complex operational environments. This limitation is particularly problematic in real-time monitoring and the efficient operation of power systems. In order to solve these problems, this paper proposes a method of arc detection based on deep learning, called arc multi-scene detection (ArcMSD), which leverages deep learning techniques to address these challenges. The primary distinction of this method lies in its enhancement of the Inception V3 model. This paper has redesigned the original Inception module by incorporating a guided anchor mechanism, an attention mechanism, and upsampling techniques to optimize detection performance. The improved Inception V3 network uses an attention mechanism to allow the model to focus on arc features in complex backgrounds, which can also prevent the model from overfitting. It performs upsampling and fusion with low-level features in the model. The fused features have better arc discrimination capabilities than the original input features, which better improves the accuracy of the model. In order to adapt to arcs with large size differences and improve detection efficiency, the guided anchor is selected to adjust the anchor generation algorithm. In terms of dataset, continuous frame images are intercepted from the video of Integrated Supervision and Control System (ISCS), and image preprocessing operations are performed to improve the model’s detection accuracy of pantograph arcs. Experimental results show that the mean Average Precision (mAP) of the deep learning model proposed in this article is 95.4%, which is far better than other models, thus verify the method’s efficacy. |
| format | Article |
| id | doaj-art-7cd876a2fe4b4853ab2905b63b9ad3ee |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-7cd876a2fe4b4853ab2905b63b9ad3ee2025-02-02T12:17:50ZengNature PortfolioScientific Reports2045-23222025-01-0115112010.1038/s41598-025-88109-xA novel arc detection and identification method in pantograph-catenary system based on deep learningYue Yan0Hu Liu1Linfeng Gan2Runtong Zhu3School of Railway Transportation, Shanghai Institute of TechnologySchool of Railway Transportation, Shanghai Institute of TechnologySchool of Railway Transportation, Shanghai Institute of TechnologySchool of Railway Transportation, Shanghai Institute of TechnologyAbstract Arc detection is crucial for ensuring the safe operation of power systems, where timely and accurate detection of arcs can prevent potential hazards such as fires, equipment damage, or system failures. Traditional arc detection methods, while functional, often suffer from low detection accuracy and high computational complexity, especially in complex operational environments. This limitation is particularly problematic in real-time monitoring and the efficient operation of power systems. In order to solve these problems, this paper proposes a method of arc detection based on deep learning, called arc multi-scene detection (ArcMSD), which leverages deep learning techniques to address these challenges. The primary distinction of this method lies in its enhancement of the Inception V3 model. This paper has redesigned the original Inception module by incorporating a guided anchor mechanism, an attention mechanism, and upsampling techniques to optimize detection performance. The improved Inception V3 network uses an attention mechanism to allow the model to focus on arc features in complex backgrounds, which can also prevent the model from overfitting. It performs upsampling and fusion with low-level features in the model. The fused features have better arc discrimination capabilities than the original input features, which better improves the accuracy of the model. In order to adapt to arcs with large size differences and improve detection efficiency, the guided anchor is selected to adjust the anchor generation algorithm. In terms of dataset, continuous frame images are intercepted from the video of Integrated Supervision and Control System (ISCS), and image preprocessing operations are performed to improve the model’s detection accuracy of pantograph arcs. Experimental results show that the mean Average Precision (mAP) of the deep learning model proposed in this article is 95.4%, which is far better than other models, thus verify the method’s efficacy.https://doi.org/10.1038/s41598-025-88109-xArc detectionDeep learningInception V3Guided anchor |
| spellingShingle | Yue Yan Hu Liu Linfeng Gan Runtong Zhu A novel arc detection and identification method in pantograph-catenary system based on deep learning Scientific Reports Arc detection Deep learning Inception V3 Guided anchor |
| title | A novel arc detection and identification method in pantograph-catenary system based on deep learning |
| title_full | A novel arc detection and identification method in pantograph-catenary system based on deep learning |
| title_fullStr | A novel arc detection and identification method in pantograph-catenary system based on deep learning |
| title_full_unstemmed | A novel arc detection and identification method in pantograph-catenary system based on deep learning |
| title_short | A novel arc detection and identification method in pantograph-catenary system based on deep learning |
| title_sort | novel arc detection and identification method in pantograph catenary system based on deep learning |
| topic | Arc detection Deep learning Inception V3 Guided anchor |
| url | https://doi.org/10.1038/s41598-025-88109-x |
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