NAS-YOLOX: a SAR ship detection using neural architecture search and multi-scale attention
Due to the advantages of all-weather capability and high resolution, synthetic aperture radar (SAR) image ship detection has been widely applied in the military, civilian, and other domains. However, SAR-based ship detection suffers from limitations such as strong scattering of targets, multiple sca...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2023.2257399 |
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| _version_ | 1849702994546786304 |
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| author | Hao Wang Dezhi Han Mingming Cui Chongqing Chen |
| author_facet | Hao Wang Dezhi Han Mingming Cui Chongqing Chen |
| author_sort | Hao Wang |
| collection | DOAJ |
| description | Due to the advantages of all-weather capability and high resolution, synthetic aperture radar (SAR) image ship detection has been widely applied in the military, civilian, and other domains. However, SAR-based ship detection suffers from limitations such as strong scattering of targets, multiple scales, and background interference, leading to low detection accuracy. To address these limitations, this paper presents a novel SAR ship detection method, NAS-YOLOX, which leverages the efficient feature fusion of the neural architecture search feature pyramid network (NAS-FPN) and the effective feature extraction of the multi-scale attention mechanism. Specifically, NAS-FPN replaces the PAFPN in the baseline YOLOX, greatly enhances the fusion performance of the model’s multi-scale feature information, and a dilated convolution feature enhancement module (DFEM) is designed and integrated into the backbone network to improve the network’s receptive field and target information extraction capabilities. Furthermore, a multi-scale channel-spatial attention (MCSA) mechanism is conceptualised to enhance focus on target regions, improve small-scale target detection, and adapt to multi-scale targets. Additionally, extensive experiments conducted on benchmark datasets, HRSID and SSDD, demonstrate that NAS-YOLOX achieves comparable or superior performance compared to other state-of-the-art ship detection models and reaches best accuracies of 91.1% and 97.2% on AP0.5, respectively. |
| format | Article |
| id | doaj-art-0fd2339f9ecf4d90a2bf2bd7e32cbc78 |
| institution | DOAJ |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-0fd2339f9ecf4d90a2bf2bd7e32cbc782025-08-20T03:17:27ZengTaylor & Francis GroupConnection Science0954-00911360-04942023-12-0135113210.1080/09540091.2023.22573992257399NAS-YOLOX: a SAR ship detection using neural architecture search and multi-scale attentionHao Wang0Dezhi Han1Mingming Cui2Chongqing Chen3Shanghai Maritime UniversityShanghai Maritime UniversityShanghai Maritime UniversityShanghai Maritime UniversityDue to the advantages of all-weather capability and high resolution, synthetic aperture radar (SAR) image ship detection has been widely applied in the military, civilian, and other domains. However, SAR-based ship detection suffers from limitations such as strong scattering of targets, multiple scales, and background interference, leading to low detection accuracy. To address these limitations, this paper presents a novel SAR ship detection method, NAS-YOLOX, which leverages the efficient feature fusion of the neural architecture search feature pyramid network (NAS-FPN) and the effective feature extraction of the multi-scale attention mechanism. Specifically, NAS-FPN replaces the PAFPN in the baseline YOLOX, greatly enhances the fusion performance of the model’s multi-scale feature information, and a dilated convolution feature enhancement module (DFEM) is designed and integrated into the backbone network to improve the network’s receptive field and target information extraction capabilities. Furthermore, a multi-scale channel-spatial attention (MCSA) mechanism is conceptualised to enhance focus on target regions, improve small-scale target detection, and adapt to multi-scale targets. Additionally, extensive experiments conducted on benchmark datasets, HRSID and SSDD, demonstrate that NAS-YOLOX achieves comparable or superior performance compared to other state-of-the-art ship detection models and reaches best accuracies of 91.1% and 97.2% on AP0.5, respectively.http://dx.doi.org/10.1080/09540091.2023.2257399synthetic aperture radar (sar)ship detectionyou only look once version x (yolox)neural architecture search-feature pyramid network (nas-fpn) |
| spellingShingle | Hao Wang Dezhi Han Mingming Cui Chongqing Chen NAS-YOLOX: a SAR ship detection using neural architecture search and multi-scale attention Connection Science synthetic aperture radar (sar) ship detection you only look once version x (yolox) neural architecture search-feature pyramid network (nas-fpn) |
| title | NAS-YOLOX: a SAR ship detection using neural architecture search and multi-scale attention |
| title_full | NAS-YOLOX: a SAR ship detection using neural architecture search and multi-scale attention |
| title_fullStr | NAS-YOLOX: a SAR ship detection using neural architecture search and multi-scale attention |
| title_full_unstemmed | NAS-YOLOX: a SAR ship detection using neural architecture search and multi-scale attention |
| title_short | NAS-YOLOX: a SAR ship detection using neural architecture search and multi-scale attention |
| title_sort | nas yolox a sar ship detection using neural architecture search and multi scale attention |
| topic | synthetic aperture radar (sar) ship detection you only look once version x (yolox) neural architecture search-feature pyramid network (nas-fpn) |
| url | http://dx.doi.org/10.1080/09540091.2023.2257399 |
| work_keys_str_mv | AT haowang nasyoloxasarshipdetectionusingneuralarchitecturesearchandmultiscaleattention AT dezhihan nasyoloxasarshipdetectionusingneuralarchitecturesearchandmultiscaleattention AT mingmingcui nasyoloxasarshipdetectionusingneuralarchitecturesearchandmultiscaleattention AT chongqingchen nasyoloxasarshipdetectionusingneuralarchitecturesearchandmultiscaleattention |