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| Real-Time Detection of Underwater Fish Based on Improved YOLO and Transfer Learning |
| LI Qingzhong1, LI Yibing1, NIU Jiong1 |
| 1.College of Engineering, Ocean University of China, Qingdao 266100 |
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Abstract To fast detect underwater fish in unrestricted underwater environment based on underwater video collected by underwater robots, a real-time detection algorithm for underwater fish based on improved you only look once(YOLO) and transfer learning is proposed. Firstly, an underwater-YOLO for the embedding computer system of underwater robots is designed to overcome the shortcomings of traditional YOLO. Then, transfer learning strategy is employed to train the underwater-YOLO network and alleviate the limitation of known underwater fish samples. A preprocessing algorithm based on contrast limited adaptive histogram equalization is proposed to overcome the problem of underwater image degradation. Finally, a video frame selection method for foreground computation of underwater-YOLO based on structure similarity between inter-frames is proposed to increase the detection frame rate. The experimental results show that the proposed algorithm achieves the goal of real-time detection of underwater fish in unconstrained underwater environment. Compared with the traditional YOLO,the proposed underwater-YOLO generates better detection performance in complex scenes with small fish and overlapped fishes.
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Received: 14 November 2018
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| Fund:05202), National Natural Science Foundation of China(No.61132005), National Marine Technology Program for Public Welfare of China (No.201605002) |
About author:: (LI Qingzhong, Ph.D., professor. His research interests include image processing, signal processing and pattern recognition.)
(NIU Jiong, Ph.D. candidate, engineer. His research interests include high frequency radar signal processing and high frequency radar marine environment monitoring technology.) |
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2019, Vol. 32 
