Affected by strong underwater light attenuation or scattering, underwater images have problems such as color distortion, blur, and loss of detail, which seriously affect the accuracy of underwater target recognition. To address the above problems, this paper proposes a scheme that combines image enhancement for turbid waters and the YOLOv4 algorithm. First, the improved multi-scale Retinex with color restoration is used to enhance the underwater image, and then the fully convolutional generative adversarial network is used to achieve image color correction and detail restoration. Finally, the enhanced image is used for fish target recognition through YOLOv4 algorithm. The results show that the mAP (mean Average Precision) of the proposed method combining the image enhancement method with the YOLOv4 algorithm can reach 89.59%, which is 7.46% higher than that of original image after training, and the detection speed reaches 90 frame·s ^-1.