Cross-Granularity Infrared Image Segmentation Network for Nighttime Marine Observations
Infrared image segmentation in marine environments is crucial for enhancing nighttime observations and ensuring maritime safety. While recent advancements in deep learning have significantly improved segmentation accuracy, challenges remain due to nighttime marine scenes including low contrast and n...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/11/2082 |
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| author | Hu Xu Yang Yu Xiaomin Zhang Ju He |
| author_facet | Hu Xu Yang Yu Xiaomin Zhang Ju He |
| author_sort | Hu Xu |
| collection | DOAJ |
| description | Infrared image segmentation in marine environments is crucial for enhancing nighttime observations and ensuring maritime safety. While recent advancements in deep learning have significantly improved segmentation accuracy, challenges remain due to nighttime marine scenes including low contrast and noise backgrounds. This paper introduces a cross-granularity infrared image segmentation network CGSegNet designed to address these challenges specifically for infrared images. The proposed method designs a hybrid feature framework with cross-granularity to enhance segmentation performance in complex water surface scenarios. To suppress feature semantic disparity against different feature granularity, we propose an adaptive multi-scale fusion module (AMF) that combines local granularity extraction with global context granularity. Additionally, incorporating a handcrafted histogram of oriented gradients (HOG) features, we designed a novel HOG feature fusion module to improve edge detection accuracy under low-contrast conditions. Comprehensive experiments conducted on the public infrared segmentation dataset demonstrate that our method outperforms state-of-the-art techniques, achieving superior segmentation results compared to professional infrared image segmentation methods. The results highlight the potential of our approach in facilitating accurate infrared image segmentation for nighttime marine observation, with implications for maritime safety and environmental monitoring. |
| format | Article |
| id | doaj-art-ef23c2dfaaff4c68984958eda3f6f7a4 |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-ef23c2dfaaff4c68984958eda3f6f7a42025-08-20T02:47:58ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-11-011211208210.3390/jmse12112082Cross-Granularity Infrared Image Segmentation Network for Nighttime Marine ObservationsHu Xu0Yang Yu1Xiaomin Zhang2Ju He3School of Marine Science and Technology, Northwestern Polytechnical University, No. 127 Youyi West Road, Beilin District, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, No. 127 Youyi West Road, Beilin District, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, No. 127 Youyi West Road, Beilin District, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, No. 127 Youyi West Road, Beilin District, Xi’an 710072, ChinaInfrared image segmentation in marine environments is crucial for enhancing nighttime observations and ensuring maritime safety. While recent advancements in deep learning have significantly improved segmentation accuracy, challenges remain due to nighttime marine scenes including low contrast and noise backgrounds. This paper introduces a cross-granularity infrared image segmentation network CGSegNet designed to address these challenges specifically for infrared images. The proposed method designs a hybrid feature framework with cross-granularity to enhance segmentation performance in complex water surface scenarios. To suppress feature semantic disparity against different feature granularity, we propose an adaptive multi-scale fusion module (AMF) that combines local granularity extraction with global context granularity. Additionally, incorporating a handcrafted histogram of oriented gradients (HOG) features, we designed a novel HOG feature fusion module to improve edge detection accuracy under low-contrast conditions. Comprehensive experiments conducted on the public infrared segmentation dataset demonstrate that our method outperforms state-of-the-art techniques, achieving superior segmentation results compared to professional infrared image segmentation methods. The results highlight the potential of our approach in facilitating accurate infrared image segmentation for nighttime marine observation, with implications for maritime safety and environmental monitoring.https://www.mdpi.com/2077-1312/12/11/2082nighttime marine observationinfrared image segmentationcross granularitydeep-learning |
| spellingShingle | Hu Xu Yang Yu Xiaomin Zhang Ju He Cross-Granularity Infrared Image Segmentation Network for Nighttime Marine Observations Journal of Marine Science and Engineering nighttime marine observation infrared image segmentation cross granularity deep-learning |
| title | Cross-Granularity Infrared Image Segmentation Network for Nighttime Marine Observations |
| title_full | Cross-Granularity Infrared Image Segmentation Network for Nighttime Marine Observations |
| title_fullStr | Cross-Granularity Infrared Image Segmentation Network for Nighttime Marine Observations |
| title_full_unstemmed | Cross-Granularity Infrared Image Segmentation Network for Nighttime Marine Observations |
| title_short | Cross-Granularity Infrared Image Segmentation Network for Nighttime Marine Observations |
| title_sort | cross granularity infrared image segmentation network for nighttime marine observations |
| topic | nighttime marine observation infrared image segmentation cross granularity deep-learning |
| url | https://www.mdpi.com/2077-1312/12/11/2082 |
| work_keys_str_mv | AT huxu crossgranularityinfraredimagesegmentationnetworkfornighttimemarineobservations AT yangyu crossgranularityinfraredimagesegmentationnetworkfornighttimemarineobservations AT xiaominzhang crossgranularityinfraredimagesegmentationnetworkfornighttimemarineobservations AT juhe crossgranularityinfraredimagesegmentationnetworkfornighttimemarineobservations |