Abstract
To solve the problem that the underwater image quality is not high, which leads to the inaccuracy of UVMS target detection based on convolutional neural network, an underwater target detection method based on WGAN is proposed. Firstly, the classic data expansion method is used to expand the data set. Then, WGAN based method UVMS is used to synthesize data enhancement to improve the performance of detection network in underwater target detection. RetinaNet is used as a target detection network, and sea cucumbers are used as a typical research target for experiments. The experimental results show that the detection accuracy of UVMS is improved by 17% in underwater target detection. The proposed method provides a good technical support for autonomous fishing of UVMS.
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