Marine Organism Detection and Classification from Underwater Vision Based on the Deep CNN Method
Seabed fishing depends on humans in common, for instance, the sea cucumber, sea urchin, and scallop fishing, which is always a very dangerous task. Considering the underwater complex environment conditions such as low temperature, dim vision, and high pressure, collecting the marine products using u...
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| Format: | Article |
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Hindawi Limited
2020-01-01
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| Series: | Mathematical Problems in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/3937580 |
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doaj-1441bcdcb0854ff987f7b99b5e92d7cb2020-11-25T02:57:27ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472020-01-01202010.1155/2020/39375803937580Marine Organism Detection and Classification from Underwater Vision Based on the Deep CNN MethodFenglei Han0Jingzheng Yao1Haitao Zhu2Chunhui Wang3College of Shipbuilding Engineering, Harbin Engineering University, Harbin, ChinaCollege of Shipbuilding Engineering, Harbin Engineering University, Harbin, ChinaCollege of Shipbuilding Engineering, Harbin Engineering University, Harbin, ChinaCollege of Shipbuilding Engineering, Harbin Engineering University, Harbin, ChinaSeabed fishing depends on humans in common, for instance, the sea cucumber, sea urchin, and scallop fishing, which is always a very dangerous task. Considering the underwater complex environment conditions such as low temperature, dim vision, and high pressure, collecting the marine products using underwater robots is commonly regarded as a feasible solution. The key technique of the underwater robot development is to detect and locate the main target from underwater vision. This research is based on the deep convolutional neural network (CNN) to realize the target recognition from underwater vision. The RPN (Region Proposal Network) is used to optimize the feature extraction capability. Deep learning dataset is prepared using an underwater video obtained from a sea cucumber fishing ROV (Remote Operated Vehicle). The inspiration of the network structure and the improvements come from the Faster RCNN and Hypernet method, and for the underwater dataset, the method proposed in this paper shows a good performance of recall and object detection accuracy. The detection runs with a speed of 17 fps on a GPU, which is applicable to be used for real-time processing.http://dx.doi.org/10.1155/2020/3937580 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Fenglei Han Jingzheng Yao Haitao Zhu Chunhui Wang |
| spellingShingle |
Fenglei Han Jingzheng Yao Haitao Zhu Chunhui Wang Marine Organism Detection and Classification from Underwater Vision Based on the Deep CNN Method Mathematical Problems in Engineering |
| author_facet |
Fenglei Han Jingzheng Yao Haitao Zhu Chunhui Wang |
| author_sort |
Fenglei Han |
| title |
Marine Organism Detection and Classification from Underwater Vision Based on the Deep CNN Method |
| title_short |
Marine Organism Detection and Classification from Underwater Vision Based on the Deep CNN Method |
| title_full |
Marine Organism Detection and Classification from Underwater Vision Based on the Deep CNN Method |
| title_fullStr |
Marine Organism Detection and Classification from Underwater Vision Based on the Deep CNN Method |
| title_full_unstemmed |
Marine Organism Detection and Classification from Underwater Vision Based on the Deep CNN Method |
| title_sort |
marine organism detection and classification from underwater vision based on the deep cnn method |
| publisher |
Hindawi Limited |
| series |
Mathematical Problems in Engineering |
| issn |
1024-123X 1563-5147 |
| publishDate |
2020-01-01 |
| description |
Seabed fishing depends on humans in common, for instance, the sea cucumber, sea urchin, and scallop fishing, which is always a very dangerous task. Considering the underwater complex environment conditions such as low temperature, dim vision, and high pressure, collecting the marine products using underwater robots is commonly regarded as a feasible solution. The key technique of the underwater robot development is to detect and locate the main target from underwater vision. This research is based on the deep convolutional neural network (CNN) to realize the target recognition from underwater vision. The RPN (Region Proposal Network) is used to optimize the feature extraction capability. Deep learning dataset is prepared using an underwater video obtained from a sea cucumber fishing ROV (Remote Operated Vehicle). The inspiration of the network structure and the improvements come from the Faster RCNN and Hypernet method, and for the underwater dataset, the method proposed in this paper shows a good performance of recall and object detection accuracy. The detection runs with a speed of 17 fps on a GPU, which is applicable to be used for real-time processing. |
| url |
http://dx.doi.org/10.1155/2020/3937580 |
| work_keys_str_mv |
AT fengleihan marineorganismdetectionandclassificationfromunderwatervisionbasedonthedeepcnnmethod AT jingzhengyao marineorganismdetectionandclassificationfromunderwatervisionbasedonthedeepcnnmethod AT haitaozhu marineorganismdetectionandclassificationfromunderwatervisionbasedonthedeepcnnmethod AT chunhuiwang marineorganismdetectionandclassificationfromunderwatervisionbasedonthedeepcnnmethod |
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