Axial-UNet++ Power Line Detection Network Based on Gated Axial Attention Mechanism
The segmentation and recognition of power lines are crucial for the UAV-based inspection of overhead power lines. To address the issues of class imbalance, low sample quantity, and long-range dependency in images, a specialized semantic segmentation network for power line segmentation called Axial-U...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/23/4585 |
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| author | Ding Hu Zihao Zheng Yafei Liu Chengkang Liu Xiaoguo Zhang |
| author_facet | Ding Hu Zihao Zheng Yafei Liu Chengkang Liu Xiaoguo Zhang |
| author_sort | Ding Hu |
| collection | DOAJ |
| description | The segmentation and recognition of power lines are crucial for the UAV-based inspection of overhead power lines. To address the issues of class imbalance, low sample quantity, and long-range dependency in images, a specialized semantic segmentation network for power line segmentation called Axial-UNet++ is proposed. Firstly, to tackle the issue of long-range dependencies in images and low sample quantity, a gated axial attention mechanism is introduced to expand the receptive field and improve the capture of relative positional biases in small datasets, thereby proposing a novel feature extraction module termed axial-channel local normalization module. Secondly, to address the imbalance in training samples, a new loss function is developed by combining traditional binary cross-entropy loss with focal loss, enhancing the precision of image semantic segmentation. Lastly, ablation and comparative experiments on the PLDU and Mendeley datasets demonstrate that the proposed model achieves 54.7% IoU and 80.1% recall on the PLDU dataset, and 79.3% IoU and 93.1% recall on the Mendeley dataset, outperforming other listed models. Additionally, robustness experiments show the adaptability of the Axial-UNet++ model under extreme conditions and the augmented image dataset used in this study has been open sourced. |
| format | Article |
| id | doaj-art-63b54c4cb6a44b61ad7ffd416fb8edd1 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-63b54c4cb6a44b61ad7ffd416fb8edd12024-12-13T16:31:21ZengMDPI AGRemote Sensing2072-42922024-12-011623458510.3390/rs16234585Axial-UNet++ Power Line Detection Network Based on Gated Axial Attention MechanismDing Hu0Zihao Zheng1Yafei Liu2Chengkang Liu3Xiaoguo Zhang4School of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaSchool of Software, Southeast University, Suzhou 215123, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaSchool of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaThe segmentation and recognition of power lines are crucial for the UAV-based inspection of overhead power lines. To address the issues of class imbalance, low sample quantity, and long-range dependency in images, a specialized semantic segmentation network for power line segmentation called Axial-UNet++ is proposed. Firstly, to tackle the issue of long-range dependencies in images and low sample quantity, a gated axial attention mechanism is introduced to expand the receptive field and improve the capture of relative positional biases in small datasets, thereby proposing a novel feature extraction module termed axial-channel local normalization module. Secondly, to address the imbalance in training samples, a new loss function is developed by combining traditional binary cross-entropy loss with focal loss, enhancing the precision of image semantic segmentation. Lastly, ablation and comparative experiments on the PLDU and Mendeley datasets demonstrate that the proposed model achieves 54.7% IoU and 80.1% recall on the PLDU dataset, and 79.3% IoU and 93.1% recall on the Mendeley dataset, outperforming other listed models. Additionally, robustness experiments show the adaptability of the Axial-UNet++ model under extreme conditions and the augmented image dataset used in this study has been open sourced.https://www.mdpi.com/2072-4292/16/23/4585gated axial attention mechanismsemantic segmentationpower line detectionUNet++loss function |
| spellingShingle | Ding Hu Zihao Zheng Yafei Liu Chengkang Liu Xiaoguo Zhang Axial-UNet++ Power Line Detection Network Based on Gated Axial Attention Mechanism Remote Sensing gated axial attention mechanism semantic segmentation power line detection UNet++ loss function |
| title | Axial-UNet++ Power Line Detection Network Based on Gated Axial Attention Mechanism |
| title_full | Axial-UNet++ Power Line Detection Network Based on Gated Axial Attention Mechanism |
| title_fullStr | Axial-UNet++ Power Line Detection Network Based on Gated Axial Attention Mechanism |
| title_full_unstemmed | Axial-UNet++ Power Line Detection Network Based on Gated Axial Attention Mechanism |
| title_short | Axial-UNet++ Power Line Detection Network Based on Gated Axial Attention Mechanism |
| title_sort | axial unet power line detection network based on gated axial attention mechanism |
| topic | gated axial attention mechanism semantic segmentation power line detection UNet++ loss function |
| url | https://www.mdpi.com/2072-4292/16/23/4585 |
| work_keys_str_mv | AT dinghu axialunetpowerlinedetectionnetworkbasedongatedaxialattentionmechanism AT zihaozheng axialunetpowerlinedetectionnetworkbasedongatedaxialattentionmechanism AT yafeiliu axialunetpowerlinedetectionnetworkbasedongatedaxialattentionmechanism AT chengkangliu axialunetpowerlinedetectionnetworkbasedongatedaxialattentionmechanism AT xiaoguozhang axialunetpowerlinedetectionnetworkbasedongatedaxialattentionmechanism |