Toward a Cluttered Environment for Learning-Based Multi-Scale Overhead Ground Wire Recognition
Abstract In this paper, we propose a learning-based real-time method to recognize and segment an overhead ground wire (OGW) from an image, which is mainly applied to the multi-scale features in a cluttered environment. The recognition and segmentation are implemented under the framework of condition...
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| Autor*in: |
Chang, Wenkai [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Neural processing letters - Springer US, 1994, 48(2018), 3 vom: 10. Feb., Seite 1789-1800 |
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| Übergeordnetes Werk: |
volume:48 ; year:2018 ; number:3 ; day:10 ; month:02 ; pages:1789-1800 |
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| DOI / URN: |
10.1007/s11063-018-9799-3 |
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10.1007/s11063-018-9799-3 doi (DE-627)OLC2044712393 (DE-He213)s11063-018-9799-3-p DE-627 ger DE-627 rakwb eng 000 VZ Chang, Wenkai verfasserin (orcid)0000-0002-1796-4186 aut Toward a Cluttered Environment for Learning-Based Multi-Scale Overhead Ground Wire Recognition 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this paper, we propose a learning-based real-time method to recognize and segment an overhead ground wire (OGW) from an image, which is mainly applied to the multi-scale features in a cluttered environment. The recognition and segmentation are implemented under the framework of conditional generative adversarial nets. The generator is an end-to-end convolutional neural network (CNN) with skip connection. The discriminator is a multi-stage CNN and learns the loss function to train the generator. The OGW is recognized and shown in the downsampling visual saliency map. Thus, the location and existence of OGW are verified, which is crucial for the detection in the cluttered environment with structural lines. Detailed experiments and comparisons are performed on real-world images to demonstrate that the proposed method significantly outperforms the traditional method. Additionally, the optimized network achieves approximately 200 fps on a graphics card (GTX970) and 30 fps on an embedded platform (Jetson TX1). Power line recognition Conditional generative adversarial nets Power line inspection Hybrid robot Yang, Guodong aut Li, En aut Liang, Zize aut Enthalten in Neural processing letters Springer US, 1994 48(2018), 3 vom: 10. Feb., Seite 1789-1800 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:48 year:2018 number:3 day:10 month:02 pages:1789-1800 https://doi.org/10.1007/s11063-018-9799-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 AR 48 2018 3 10 02 1789-1800 |
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10.1007/s11063-018-9799-3 doi (DE-627)OLC2044712393 (DE-He213)s11063-018-9799-3-p DE-627 ger DE-627 rakwb eng 000 VZ Chang, Wenkai verfasserin (orcid)0000-0002-1796-4186 aut Toward a Cluttered Environment for Learning-Based Multi-Scale Overhead Ground Wire Recognition 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this paper, we propose a learning-based real-time method to recognize and segment an overhead ground wire (OGW) from an image, which is mainly applied to the multi-scale features in a cluttered environment. The recognition and segmentation are implemented under the framework of conditional generative adversarial nets. The generator is an end-to-end convolutional neural network (CNN) with skip connection. The discriminator is a multi-stage CNN and learns the loss function to train the generator. The OGW is recognized and shown in the downsampling visual saliency map. Thus, the location and existence of OGW are verified, which is crucial for the detection in the cluttered environment with structural lines. Detailed experiments and comparisons are performed on real-world images to demonstrate that the proposed method significantly outperforms the traditional method. Additionally, the optimized network achieves approximately 200 fps on a graphics card (GTX970) and 30 fps on an embedded platform (Jetson TX1). Power line recognition Conditional generative adversarial nets Power line inspection Hybrid robot Yang, Guodong aut Li, En aut Liang, Zize aut Enthalten in Neural processing letters Springer US, 1994 48(2018), 3 vom: 10. Feb., Seite 1789-1800 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:48 year:2018 number:3 day:10 month:02 pages:1789-1800 https://doi.org/10.1007/s11063-018-9799-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 AR 48 2018 3 10 02 1789-1800 |
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10.1007/s11063-018-9799-3 doi (DE-627)OLC2044712393 (DE-He213)s11063-018-9799-3-p DE-627 ger DE-627 rakwb eng 000 VZ Chang, Wenkai verfasserin (orcid)0000-0002-1796-4186 aut Toward a Cluttered Environment for Learning-Based Multi-Scale Overhead Ground Wire Recognition 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this paper, we propose a learning-based real-time method to recognize and segment an overhead ground wire (OGW) from an image, which is mainly applied to the multi-scale features in a cluttered environment. The recognition and segmentation are implemented under the framework of conditional generative adversarial nets. The generator is an end-to-end convolutional neural network (CNN) with skip connection. The discriminator is a multi-stage CNN and learns the loss function to train the generator. The OGW is recognized and shown in the downsampling visual saliency map. Thus, the location and existence of OGW are verified, which is crucial for the detection in the cluttered environment with structural lines. Detailed experiments and comparisons are performed on real-world images to demonstrate that the proposed method significantly outperforms the traditional method. Additionally, the optimized network achieves approximately 200 fps on a graphics card (GTX970) and 30 fps on an embedded platform (Jetson TX1). Power line recognition Conditional generative adversarial nets Power line inspection Hybrid robot Yang, Guodong aut Li, En aut Liang, Zize aut Enthalten in Neural processing letters Springer US, 1994 48(2018), 3 vom: 10. Feb., Seite 1789-1800 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:48 year:2018 number:3 day:10 month:02 pages:1789-1800 https://doi.org/10.1007/s11063-018-9799-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 AR 48 2018 3 10 02 1789-1800 |
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Abstract In this paper, we propose a learning-based real-time method to recognize and segment an overhead ground wire (OGW) from an image, which is mainly applied to the multi-scale features in a cluttered environment. The recognition and segmentation are implemented under the framework of conditional generative adversarial nets. The generator is an end-to-end convolutional neural network (CNN) with skip connection. The discriminator is a multi-stage CNN and learns the loss function to train the generator. The OGW is recognized and shown in the downsampling visual saliency map. Thus, the location and existence of OGW are verified, which is crucial for the detection in the cluttered environment with structural lines. Detailed experiments and comparisons are performed on real-world images to demonstrate that the proposed method significantly outperforms the traditional method. Additionally, the optimized network achieves approximately 200 fps on a graphics card (GTX970) and 30 fps on an embedded platform (Jetson TX1). © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Abstract In this paper, we propose a learning-based real-time method to recognize and segment an overhead ground wire (OGW) from an image, which is mainly applied to the multi-scale features in a cluttered environment. The recognition and segmentation are implemented under the framework of conditional generative adversarial nets. The generator is an end-to-end convolutional neural network (CNN) with skip connection. The discriminator is a multi-stage CNN and learns the loss function to train the generator. The OGW is recognized and shown in the downsampling visual saliency map. Thus, the location and existence of OGW are verified, which is crucial for the detection in the cluttered environment with structural lines. Detailed experiments and comparisons are performed on real-world images to demonstrate that the proposed method significantly outperforms the traditional method. Additionally, the optimized network achieves approximately 200 fps on a graphics card (GTX970) and 30 fps on an embedded platform (Jetson TX1). © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Abstract In this paper, we propose a learning-based real-time method to recognize and segment an overhead ground wire (OGW) from an image, which is mainly applied to the multi-scale features in a cluttered environment. The recognition and segmentation are implemented under the framework of conditional generative adversarial nets. The generator is an end-to-end convolutional neural network (CNN) with skip connection. The discriminator is a multi-stage CNN and learns the loss function to train the generator. The OGW is recognized and shown in the downsampling visual saliency map. Thus, the location and existence of OGW are verified, which is crucial for the detection in the cluttered environment with structural lines. Detailed experiments and comparisons are performed on real-world images to demonstrate that the proposed method significantly outperforms the traditional method. Additionally, the optimized network achieves approximately 200 fps on a graphics card (GTX970) and 30 fps on an embedded platform (Jetson TX1). © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Toward a Cluttered Environment for Learning-Based Multi-Scale Overhead Ground Wire Recognition |
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Yang, Guodong Li, En Liang, Zize |
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