A real-time detection method for multi-scale ships in complex scenes
Ship detection plays an important role in tasks such as military reconnaissance, maritime target tracking, and maritime traffic control.However, due to the influence of variable sizes of ships and complex background of sea surface, detecting multi-scale ships remains a challenge in complex sea surfa...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2022-10-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022258/ |
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| _version_ | 1841530724969086976 |
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| author | Weina ZHOU Lu LIU |
| author_facet | Weina ZHOU Lu LIU |
| author_sort | Weina ZHOU |
| collection | DOAJ |
| description | Ship detection plays an important role in tasks such as military reconnaissance, maritime target tracking, and maritime traffic control.However, due to the influence of variable sizes of ships and complex background of sea surface, detecting multi-scale ships remains a challenge in complex sea surfaces.To solve this problem, an improved YOLOv4 method based on multi-layers information interactive fusion and attention mechanism was proposed.Multi-layers information interactive fusion (MLIF) and multi-attention receptive field (MARF) were applied and combined reasonably to build a bidirectional fine-grained feature pyramid.MLIF was used to fuse feature of different scales, which not only concatenated high-level semantic features from deep layers, but also reshaped richer features from shallower layers.MARF consisted of receptive field block (RFB) and attention mechanism module, which effectively emphasized the important features and suppressed unnecessary ones.In addition, to further evaluate the performance of the proposed method, experiments were carried out on Singapore maritime dataset (SMD).The results illustrate that the method proposed can effectively solve the problem of difficult detection of multi-scale ships in complex marine environment, and meet the real-time requirements at the same time. |
| format | Article |
| id | doaj-art-1ce444eb373b40f2b2bea898a82c0dc2 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2022-10-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-1ce444eb373b40f2b2bea898a82c0dc22025-01-15T02:59:59ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012022-10-0138677859576218A real-time detection method for multi-scale ships in complex scenesWeina ZHOULu LIUShip detection plays an important role in tasks such as military reconnaissance, maritime target tracking, and maritime traffic control.However, due to the influence of variable sizes of ships and complex background of sea surface, detecting multi-scale ships remains a challenge in complex sea surfaces.To solve this problem, an improved YOLOv4 method based on multi-layers information interactive fusion and attention mechanism was proposed.Multi-layers information interactive fusion (MLIF) and multi-attention receptive field (MARF) were applied and combined reasonably to build a bidirectional fine-grained feature pyramid.MLIF was used to fuse feature of different scales, which not only concatenated high-level semantic features from deep layers, but also reshaped richer features from shallower layers.MARF consisted of receptive field block (RFB) and attention mechanism module, which effectively emphasized the important features and suppressed unnecessary ones.In addition, to further evaluate the performance of the proposed method, experiments were carried out on Singapore maritime dataset (SMD).The results illustrate that the method proposed can effectively solve the problem of difficult detection of multi-scale ships in complex marine environment, and meet the real-time requirements at the same time.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022258/multi-scale ship detectionmulti-layers information interactive fusionmulti-attention receptive fieldbidirectional fine-grained feature pyramid |
| spellingShingle | Weina ZHOU Lu LIU A real-time detection method for multi-scale ships in complex scenes Dianxin kexue multi-scale ship detection multi-layers information interactive fusion multi-attention receptive field bidirectional fine-grained feature pyramid |
| title | A real-time detection method for multi-scale ships in complex scenes |
| title_full | A real-time detection method for multi-scale ships in complex scenes |
| title_fullStr | A real-time detection method for multi-scale ships in complex scenes |
| title_full_unstemmed | A real-time detection method for multi-scale ships in complex scenes |
| title_short | A real-time detection method for multi-scale ships in complex scenes |
| title_sort | real time detection method for multi scale ships in complex scenes |
| topic | multi-scale ship detection multi-layers information interactive fusion multi-attention receptive field bidirectional fine-grained feature pyramid |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022258/ |
| work_keys_str_mv | AT weinazhou arealtimedetectionmethodformultiscaleshipsincomplexscenes AT luliu arealtimedetectionmethodformultiscaleshipsincomplexscenes AT weinazhou realtimedetectionmethodformultiscaleshipsincomplexscenes AT luliu realtimedetectionmethodformultiscaleshipsincomplexscenes |