Ship Target Detection Algorithm Based on Improved YOLOv3 for Maritime Image
Accurate identification of ships is the key technology of intelligent transportation in water. At the same time, it also provides a judgment basis for water traffic safety control. This paper proposed a detection method of ships in water based on improved You Only Look Once version 3 (YOLOv3), which...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/9440212 |
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| _version_ | 1832566255260794880 |
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| author | Dehai Chen Shiru Sun Zhijun Lei Heng Shao Yuzhao Wang |
| author_facet | Dehai Chen Shiru Sun Zhijun Lei Heng Shao Yuzhao Wang |
| author_sort | Dehai Chen |
| collection | DOAJ |
| description | Accurate identification of ships is the key technology of intelligent transportation in water. At the same time, it also provides a judgment basis for water traffic safety control. This paper proposed a detection method of ships in water based on improved You Only Look Once version 3 (YOLOv3), which is called Feature Attention, Feature Enhancement YOLOv3 (AE-YOLOv3). The feature attention module was constructed by introducing the attention mechanism, which was embedded in Darknet-53 for feature recalibration, which improved the feature extraction ability of the model in the complex navigable background. For the problem of insufficient semantic information of low-level features in the feature fusion process, a feature enhancement module was constructed and applied to the feature fusion part to enhance the receptive field size of the corresponding feature layer and the correlation degree of feature extraction network. Experiments were carried out on the public SeaShips dataset. Experiments show that the detection accuracy is as high as 98.72%, which is better than that of other mainstream ship identification models, fully verifying the superiority of this method in the detection of waterborne traffic ships. |
| format | Article |
| id | doaj-art-027f3b639c9c4e5fbfe92ae323ce0c95 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-027f3b639c9c4e5fbfe92ae323ce0c952025-02-03T01:04:32ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/94402129440212Ship Target Detection Algorithm Based on Improved YOLOv3 for Maritime ImageDehai Chen0Shiru Sun1Zhijun Lei2Heng Shao3Yuzhao Wang4School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaSchool of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaSchool of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaSchool of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaSchool of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaAccurate identification of ships is the key technology of intelligent transportation in water. At the same time, it also provides a judgment basis for water traffic safety control. This paper proposed a detection method of ships in water based on improved You Only Look Once version 3 (YOLOv3), which is called Feature Attention, Feature Enhancement YOLOv3 (AE-YOLOv3). The feature attention module was constructed by introducing the attention mechanism, which was embedded in Darknet-53 for feature recalibration, which improved the feature extraction ability of the model in the complex navigable background. For the problem of insufficient semantic information of low-level features in the feature fusion process, a feature enhancement module was constructed and applied to the feature fusion part to enhance the receptive field size of the corresponding feature layer and the correlation degree of feature extraction network. Experiments were carried out on the public SeaShips dataset. Experiments show that the detection accuracy is as high as 98.72%, which is better than that of other mainstream ship identification models, fully verifying the superiority of this method in the detection of waterborne traffic ships.http://dx.doi.org/10.1155/2021/9440212 |
| spellingShingle | Dehai Chen Shiru Sun Zhijun Lei Heng Shao Yuzhao Wang Ship Target Detection Algorithm Based on Improved YOLOv3 for Maritime Image Journal of Advanced Transportation |
| title | Ship Target Detection Algorithm Based on Improved YOLOv3 for Maritime Image |
| title_full | Ship Target Detection Algorithm Based on Improved YOLOv3 for Maritime Image |
| title_fullStr | Ship Target Detection Algorithm Based on Improved YOLOv3 for Maritime Image |
| title_full_unstemmed | Ship Target Detection Algorithm Based on Improved YOLOv3 for Maritime Image |
| title_short | Ship Target Detection Algorithm Based on Improved YOLOv3 for Maritime Image |
| title_sort | ship target detection algorithm based on improved yolov3 for maritime image |
| url | http://dx.doi.org/10.1155/2021/9440212 |
| work_keys_str_mv | AT dehaichen shiptargetdetectionalgorithmbasedonimprovedyolov3formaritimeimage AT shirusun shiptargetdetectionalgorithmbasedonimprovedyolov3formaritimeimage AT zhijunlei shiptargetdetectionalgorithmbasedonimprovedyolov3formaritimeimage AT hengshao shiptargetdetectionalgorithmbasedonimprovedyolov3formaritimeimage AT yuzhaowang shiptargetdetectionalgorithmbasedonimprovedyolov3formaritimeimage |