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.
李庆忠,李宜兵,牛炯. 基于改进YOLO和迁移学习的水下鱼类目标实时检测[J]. 模式识别与人工智能, 2019, 32(3): 193-203.
LI Qingzhong, LI Yibing, NIU Jiong. Real-Time Detection of Underwater Fish Based on Improved YOLO and Transfer Learning. , 2019, 32(3): 193-203.
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2019, Vol. 32 
