A Metric Learning-Based Improved Oriented R-CNN for Wildfire Detection in Power Transmission Corridors
Wildfire detection in power transmission corridors is essential for providing timely warnings and ensuring the safe and stable operation of power lines. However, this task faces significant challenges due to the large number of smoke-like samples in the background, the complex and diverse target mor...
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MDPI AG
2025-06-01
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| author | Xiaole Wang Bo Wang Peng Luo Leixiong Wang Yurou Wu |
| author_facet | Xiaole Wang Bo Wang Peng Luo Leixiong Wang Yurou Wu |
| author_sort | Xiaole Wang |
| collection | DOAJ |
| description | Wildfire detection in power transmission corridors is essential for providing timely warnings and ensuring the safe and stable operation of power lines. However, this task faces significant challenges due to the large number of smoke-like samples in the background, the complex and diverse target morphologies, and the difficulty of detecting small-scale smoke and flame objects. To address these issues, this paper proposed an improved Oriented R-CNN model enhanced with metric learning for wildfire detection in power transmission corridors. Specifically, a multi-center metric loss (MCM-Loss) module based on metric learning was introduced to enhance the model’s ability to differentiate features of similar targets, thereby improving the recognition accuracy in the presence of interference. Experimental results showed that the introduction of the MCM-Loss module increased the average precision (AP) for smoke targets by 2.7%. In addition, the group convolution-based network ResNeXt was adopted to replace the original backbone network ResNet, broadening the channel dimensions of the feature extraction network and enhancing the model’s capability to detect flame and smoke targets with diverse morphologies. This substitution led to a 0.6% improvement in mean average precision (mAP). Furthermore, an FPN-CARAFE module was designed by incorporating the content-aware up-sampling operator CARAFE, which improved multi-scale feature representation and significantly boosted performance in detecting small targets. In particular, the proposed FPN-CARAFE module improved the AP for fire targets by 8.1%. Experimental results demonstrated that the proposed model achieved superior performance in wildfire detection within power transmission corridors, achieving a mAP of 90.4% on the test dataset—an improvement of 6.4% over the baseline model. Compared with other commonly used object detection algorithms, the model developed in this study exhibited improved detection performance on the test dataset, offering research support for wildfire monitoring in power transmission corridors. |
| format | Article |
| id | doaj-art-cff9dfd4ed764bada7bd38acb58b0b54 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-cff9dfd4ed764bada7bd38acb58b0b542025-08-20T03:50:17ZengMDPI AGSensors1424-82202025-06-012513388210.3390/s25133882A Metric Learning-Based Improved Oriented R-CNN for Wildfire Detection in Power Transmission CorridorsXiaole Wang0Bo Wang1Peng Luo2Leixiong Wang3Yurou Wu4School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaSchool of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaSchool of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaWuhan Power Supply Company, State Grid Hubei Electric Power Company, Wuhan 430013, ChinaWuhan Power Supply Company, State Grid Hubei Electric Power Company, Wuhan 430013, ChinaWildfire detection in power transmission corridors is essential for providing timely warnings and ensuring the safe and stable operation of power lines. However, this task faces significant challenges due to the large number of smoke-like samples in the background, the complex and diverse target morphologies, and the difficulty of detecting small-scale smoke and flame objects. To address these issues, this paper proposed an improved Oriented R-CNN model enhanced with metric learning for wildfire detection in power transmission corridors. Specifically, a multi-center metric loss (MCM-Loss) module based on metric learning was introduced to enhance the model’s ability to differentiate features of similar targets, thereby improving the recognition accuracy in the presence of interference. Experimental results showed that the introduction of the MCM-Loss module increased the average precision (AP) for smoke targets by 2.7%. In addition, the group convolution-based network ResNeXt was adopted to replace the original backbone network ResNet, broadening the channel dimensions of the feature extraction network and enhancing the model’s capability to detect flame and smoke targets with diverse morphologies. This substitution led to a 0.6% improvement in mean average precision (mAP). Furthermore, an FPN-CARAFE module was designed by incorporating the content-aware up-sampling operator CARAFE, which improved multi-scale feature representation and significantly boosted performance in detecting small targets. In particular, the proposed FPN-CARAFE module improved the AP for fire targets by 8.1%. Experimental results demonstrated that the proposed model achieved superior performance in wildfire detection within power transmission corridors, achieving a mAP of 90.4% on the test dataset—an improvement of 6.4% over the baseline model. Compared with other commonly used object detection algorithms, the model developed in this study exhibited improved detection performance on the test dataset, offering research support for wildfire monitoring in power transmission corridors.https://www.mdpi.com/1424-8220/25/13/3882wildfire detectiontransmission corridorsmetric learninggroup convolutionscontent-aware up-sampling |
| spellingShingle | Xiaole Wang Bo Wang Peng Luo Leixiong Wang Yurou Wu A Metric Learning-Based Improved Oriented R-CNN for Wildfire Detection in Power Transmission Corridors Sensors wildfire detection transmission corridors metric learning group convolutions content-aware up-sampling |
| title | A Metric Learning-Based Improved Oriented R-CNN for Wildfire Detection in Power Transmission Corridors |
| title_full | A Metric Learning-Based Improved Oriented R-CNN for Wildfire Detection in Power Transmission Corridors |
| title_fullStr | A Metric Learning-Based Improved Oriented R-CNN for Wildfire Detection in Power Transmission Corridors |
| title_full_unstemmed | A Metric Learning-Based Improved Oriented R-CNN for Wildfire Detection in Power Transmission Corridors |
| title_short | A Metric Learning-Based Improved Oriented R-CNN for Wildfire Detection in Power Transmission Corridors |
| title_sort | metric learning based improved oriented r cnn for wildfire detection in power transmission corridors |
| topic | wildfire detection transmission corridors metric learning group convolutions content-aware up-sampling |
| url | https://www.mdpi.com/1424-8220/25/13/3882 |
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