Modified Yolov3 for Ship Detection with Visible and Infrared Images
As the demands for international marine transportation increase rapidly, effective port management has become an important issue. Automatic ship recognition can facilitate the realization of smart ports, and improve the efficiency of port operation and management. In order to take into account the p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/5/739 |
| _version_ | 1797475373912424448 |
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| author | Lena Chang Yi-Ting Chen Jung-Hua Wang Yang-Lang Chang |
| author_facet | Lena Chang Yi-Ting Chen Jung-Hua Wang Yang-Lang Chang |
| author_sort | Lena Chang |
| collection | DOAJ |
| description | As the demands for international marine transportation increase rapidly, effective port management has become an important issue. Automatic ship recognition can facilitate the realization of smart ports, and improve the efficiency of port operation and management. In order to take into account the processing efficiency and detection accuracy at the same time, the study presented an improved deep-learning network based on You only look once version 3 (Yolov3) for all-day ship detection with visible and infrared images. Yolov3 network can simultaneously improve the recognition ability of large and small objects through multiscale feature-extraction architecture. Considering reducing computational time and network complexity with relatively competitive detection accuracy, the study modified the architecture of Yolov3 by choosing an appropriate input image size, fewer convolution filters, and detection scales. In addition, the reduced Yolov3 was further modified with the spatial pyramid pooling (SPP) module to improve the network performance in feature extraction. Therefore, the proposed modified network can achieve the purpose of multi-scale, multi-type, and multi-resolution ship detection. In the study, a common self-built data set was introduced, aiming to conduct all-day and real-time ship detection. The data set included a total of 5557 infrared and visible light images from six common ship types in northern Taiwan ports. The experimental results on the data set showed that the proposed modified network architecture achieved acceptable performance in ship detection, with the mean average precision (mAP) of 93.2%, processing 104 frames per second (FPS), and 29.2 billion floating point operations (BFLOPs). Compared with the original Yolov3, the proposed method can increase mAP and FPS by about 5.8% and 8%, respectively, while reducing BFLOPs by about 47.5%. Furthermore, the computational efficiency and detection performance of the proposed approach have been verified in the comparative experiments with some existing convolutional neural networks (CNNs). In conclusion, the proposed method can achieve high detection accuracy with lower computational costs compared to other networks. |
| first_indexed | 2024-03-09T20:43:15Z |
| format | Article |
| id | doaj.art-ec9cbf93502b4d02b1b93ebc7a07c518 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T20:43:15Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-ec9cbf93502b4d02b1b93ebc7a07c5182023-11-23T22:53:16ZengMDPI AGElectronics2079-92922022-02-0111573910.3390/electronics11050739Modified Yolov3 for Ship Detection with Visible and Infrared ImagesLena Chang0Yi-Ting Chen1Jung-Hua Wang2Yang-Lang Chang3Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, Keelung 202301, TaiwanDepartment of Electrical Engineering, National Taiwan Ocean University, Keelung 202301, TaiwanDepartment of Electrical Engineering, National Taiwan Ocean University, Keelung 202301, TaiwanDepartment of Electrical Engineering, National Taipei University of Technology, Taipei 106344, TaiwanAs the demands for international marine transportation increase rapidly, effective port management has become an important issue. Automatic ship recognition can facilitate the realization of smart ports, and improve the efficiency of port operation and management. In order to take into account the processing efficiency and detection accuracy at the same time, the study presented an improved deep-learning network based on You only look once version 3 (Yolov3) for all-day ship detection with visible and infrared images. Yolov3 network can simultaneously improve the recognition ability of large and small objects through multiscale feature-extraction architecture. Considering reducing computational time and network complexity with relatively competitive detection accuracy, the study modified the architecture of Yolov3 by choosing an appropriate input image size, fewer convolution filters, and detection scales. In addition, the reduced Yolov3 was further modified with the spatial pyramid pooling (SPP) module to improve the network performance in feature extraction. Therefore, the proposed modified network can achieve the purpose of multi-scale, multi-type, and multi-resolution ship detection. In the study, a common self-built data set was introduced, aiming to conduct all-day and real-time ship detection. The data set included a total of 5557 infrared and visible light images from six common ship types in northern Taiwan ports. The experimental results on the data set showed that the proposed modified network architecture achieved acceptable performance in ship detection, with the mean average precision (mAP) of 93.2%, processing 104 frames per second (FPS), and 29.2 billion floating point operations (BFLOPs). Compared with the original Yolov3, the proposed method can increase mAP and FPS by about 5.8% and 8%, respectively, while reducing BFLOPs by about 47.5%. Furthermore, the computational efficiency and detection performance of the proposed approach have been verified in the comparative experiments with some existing convolutional neural networks (CNNs). In conclusion, the proposed method can achieve high detection accuracy with lower computational costs compared to other networks.https://www.mdpi.com/2079-9292/11/5/739ship detectionYolov3spatial pyramid poolinginfrared imagesvisible images |
| spellingShingle | Lena Chang Yi-Ting Chen Jung-Hua Wang Yang-Lang Chang Modified Yolov3 for Ship Detection with Visible and Infrared Images Electronics ship detection Yolov3 spatial pyramid pooling infrared images visible images |
| title | Modified Yolov3 for Ship Detection with Visible and Infrared Images |
| title_full | Modified Yolov3 for Ship Detection with Visible and Infrared Images |
| title_fullStr | Modified Yolov3 for Ship Detection with Visible and Infrared Images |
| title_full_unstemmed | Modified Yolov3 for Ship Detection with Visible and Infrared Images |
| title_short | Modified Yolov3 for Ship Detection with Visible and Infrared Images |
| title_sort | modified yolov3 for ship detection with visible and infrared images |
| topic | ship detection Yolov3 spatial pyramid pooling infrared images visible images |
| url | https://www.mdpi.com/2079-9292/11/5/739 |
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