Ship Detection under Low-Visibility Weather Interference via an Ensemble Generative Adversarial Network
Maritime ship detection plays a crucial role in smart ships and intelligent transportation systems. However, adverse maritime weather conditions, such as rain streak and fog, can significantly impair the performance of visual systems for maritime traffic. These factors constrain the performance of t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/11/2065 |
| _version_ | 1797458761448685568 |
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| author | Xinqiang Chen Chenxin Wei Zhengang Xin Jiansen Zhao Jiangfeng Xian |
| author_facet | Xinqiang Chen Chenxin Wei Zhengang Xin Jiansen Zhao Jiangfeng Xian |
| author_sort | Xinqiang Chen |
| collection | DOAJ |
| description | Maritime ship detection plays a crucial role in smart ships and intelligent transportation systems. However, adverse maritime weather conditions, such as rain streak and fog, can significantly impair the performance of visual systems for maritime traffic. These factors constrain the performance of traffic monitoring systems and ship-detection algorithms for autonomous ship navigation, affecting maritime safety. The paper proposes an approach to resolve the problem by visually removing rain streaks and fog from images, achieving an integrated framework for accurate ship detection. Firstly, the paper employs an attention generation network within an adversarial neural network to focus on the distorted regions of the degraded images. The paper also utilizes a contextual encoder to infer contextual information within the distorted regions, enhancing the credibility of image restoration. Secondly, a weighted bidirectional feature pyramid network (BiFPN) is introduced to achieve rapid multi-scale feature fusion, enhancing the accuracy of maritime ship detection. The proposed GYB framework was validated using the SeaShip dataset. The experimental results show that the proposed framework achieves an average accuracy of 96.3%, a recall of 95.35%, and a harmonic mean of 95.85% in detecting maritime traffic ships under rain-streak and foggy-weather conditions. Moreover, the framework outperforms state-of-the-art ship detection methods in such challenging weather scenarios. |
| first_indexed | 2024-03-09T16:41:50Z |
| format | Article |
| id | doaj.art-31d81049981a47e0b02b54902a0cbc52 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T16:41:50Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-31d81049981a47e0b02b54902a0cbc522023-11-24T14:50:17ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-10-011111206510.3390/jmse11112065Ship Detection under Low-Visibility Weather Interference via an Ensemble Generative Adversarial NetworkXinqiang Chen0Chenxin Wei1Zhengang Xin2Jiansen Zhao3Jiangfeng Xian4Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, ChinaInstitute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, ChinaInstitute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, ChinaMerchant Marine College, Shanghai Maritime University, Shanghai 201306, ChinaInstitute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, ChinaMaritime ship detection plays a crucial role in smart ships and intelligent transportation systems. However, adverse maritime weather conditions, such as rain streak and fog, can significantly impair the performance of visual systems for maritime traffic. These factors constrain the performance of traffic monitoring systems and ship-detection algorithms for autonomous ship navigation, affecting maritime safety. The paper proposes an approach to resolve the problem by visually removing rain streaks and fog from images, achieving an integrated framework for accurate ship detection. Firstly, the paper employs an attention generation network within an adversarial neural network to focus on the distorted regions of the degraded images. The paper also utilizes a contextual encoder to infer contextual information within the distorted regions, enhancing the credibility of image restoration. Secondly, a weighted bidirectional feature pyramid network (BiFPN) is introduced to achieve rapid multi-scale feature fusion, enhancing the accuracy of maritime ship detection. The proposed GYB framework was validated using the SeaShip dataset. The experimental results show that the proposed framework achieves an average accuracy of 96.3%, a recall of 95.35%, and a harmonic mean of 95.85% in detecting maritime traffic ships under rain-streak and foggy-weather conditions. Moreover, the framework outperforms state-of-the-art ship detection methods in such challenging weather scenarios.https://www.mdpi.com/2077-1312/11/11/2065ship detectionadverse weatherimage restorationimproved YOLOv5intelligent maritime transportation |
| spellingShingle | Xinqiang Chen Chenxin Wei Zhengang Xin Jiansen Zhao Jiangfeng Xian Ship Detection under Low-Visibility Weather Interference via an Ensemble Generative Adversarial Network Journal of Marine Science and Engineering ship detection adverse weather image restoration improved YOLOv5 intelligent maritime transportation |
| title | Ship Detection under Low-Visibility Weather Interference via an Ensemble Generative Adversarial Network |
| title_full | Ship Detection under Low-Visibility Weather Interference via an Ensemble Generative Adversarial Network |
| title_fullStr | Ship Detection under Low-Visibility Weather Interference via an Ensemble Generative Adversarial Network |
| title_full_unstemmed | Ship Detection under Low-Visibility Weather Interference via an Ensemble Generative Adversarial Network |
| title_short | Ship Detection under Low-Visibility Weather Interference via an Ensemble Generative Adversarial Network |
| title_sort | ship detection under low visibility weather interference via an ensemble generative adversarial network |
| topic | ship detection adverse weather image restoration improved YOLOv5 intelligent maritime transportation |
| url | https://www.mdpi.com/2077-1312/11/11/2065 |
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