An Intelligent Ship Image/Video Detection and Classification Method with Improved Regressive Deep Convolutional Neural Network
The shipping industry is developing towards intelligence rapidly. An accurate and fast method for ship image/video detection and classification is of great significance for not only the port management, but also the safe driving of Unmanned Surface Vehicle (USV). Thus, this paper makes a self-built...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/1520872 |
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| _version_ | 1832567500726861824 |
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| author | Zhijian Huang Bowen Sui Jiayi Wen Guohe Jiang |
| author_facet | Zhijian Huang Bowen Sui Jiayi Wen Guohe Jiang |
| author_sort | Zhijian Huang |
| collection | DOAJ |
| description | The shipping industry is developing towards intelligence rapidly. An accurate and fast method for ship image/video detection and classification is of great significance for not only the port management, but also the safe driving of Unmanned Surface Vehicle (USV). Thus, this paper makes a self-built dataset for the ship image/video detection and classification, and its method based on an improved regressive deep convolutional neural network is presented. This method promotes the regressive convolutional neural network from four aspects. First, the feature extraction layer is lightweighted by referring to YOLOv2. Second, a new feature pyramid network layer is designed by improving its structure in YOLOv3. Third, a proper frame and scale suitable for ships are designed with a clustering algorithm to reduced 60% anchors. Last, the activation function is verified and optimized. Then, the detecting experiment on 7 types of ships shows that the proposed method has advantage compared with the YOLO series networks and other intelligent methods. This method can solve the problem of low recognition rate and real-time performance for ship image/video detection and classification with a small dataset. On the testing-set, the final mAP is 0.9209, the Recall is 0.9818, the AIOU is 0.7991, and the FPS is 78–80 in video detection. Thus, this method provides a highly accurate and real-time ship detection method for the intelligent port management and visual processing of the USV. In addition, the proposed regressive deep convolutional network also has a better comprehensive performance than that of YOLOv2/v3. |
| format | Article |
| id | doaj-art-2d4d43c7834f4488b274af12b8cf55f0 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-2d4d43c7834f4488b274af12b8cf55f02025-02-03T01:01:22ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/15208721520872An Intelligent Ship Image/Video Detection and Classification Method with Improved Regressive Deep Convolutional Neural NetworkZhijian Huang0Bowen Sui1Jiayi Wen2Guohe Jiang3Lab of Intelligent Control and Computation, Shanghai Maritime University, Shanghai 201306, ChinaLab of Intelligent Control and Computation, Shanghai Maritime University, Shanghai 201306, ChinaLab of Intelligent Control and Computation, Shanghai Maritime University, Shanghai 201306, ChinaLab of Intelligent Control and Computation, Shanghai Maritime University, Shanghai 201306, ChinaThe shipping industry is developing towards intelligence rapidly. An accurate and fast method for ship image/video detection and classification is of great significance for not only the port management, but also the safe driving of Unmanned Surface Vehicle (USV). Thus, this paper makes a self-built dataset for the ship image/video detection and classification, and its method based on an improved regressive deep convolutional neural network is presented. This method promotes the regressive convolutional neural network from four aspects. First, the feature extraction layer is lightweighted by referring to YOLOv2. Second, a new feature pyramid network layer is designed by improving its structure in YOLOv3. Third, a proper frame and scale suitable for ships are designed with a clustering algorithm to reduced 60% anchors. Last, the activation function is verified and optimized. Then, the detecting experiment on 7 types of ships shows that the proposed method has advantage compared with the YOLO series networks and other intelligent methods. This method can solve the problem of low recognition rate and real-time performance for ship image/video detection and classification with a small dataset. On the testing-set, the final mAP is 0.9209, the Recall is 0.9818, the AIOU is 0.7991, and the FPS is 78–80 in video detection. Thus, this method provides a highly accurate and real-time ship detection method for the intelligent port management and visual processing of the USV. In addition, the proposed regressive deep convolutional network also has a better comprehensive performance than that of YOLOv2/v3.http://dx.doi.org/10.1155/2020/1520872 |
| spellingShingle | Zhijian Huang Bowen Sui Jiayi Wen Guohe Jiang An Intelligent Ship Image/Video Detection and Classification Method with Improved Regressive Deep Convolutional Neural Network Complexity |
| title | An Intelligent Ship Image/Video Detection and Classification Method with Improved Regressive Deep Convolutional Neural Network |
| title_full | An Intelligent Ship Image/Video Detection and Classification Method with Improved Regressive Deep Convolutional Neural Network |
| title_fullStr | An Intelligent Ship Image/Video Detection and Classification Method with Improved Regressive Deep Convolutional Neural Network |
| title_full_unstemmed | An Intelligent Ship Image/Video Detection and Classification Method with Improved Regressive Deep Convolutional Neural Network |
| title_short | An Intelligent Ship Image/Video Detection and Classification Method with Improved Regressive Deep Convolutional Neural Network |
| title_sort | intelligent ship image video detection and classification method with improved regressive deep convolutional neural network |
| url | http://dx.doi.org/10.1155/2020/1520872 |
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