To address the low accuracy of underwater fish target detection caused by blurred and color-distorted underwater images, complex underwater scenes, and limited target feature extraction ability, this study proposes an improved underwater fish target detection algorithm based on YOLOv5. Firstly, in response to the blurring and color distortion of underwater images, the underwater dark channel prior (UDCP) algorithm is introduced to pre-process the images, which is helpful for correctly identifying the target in different environments. Then, considering the problems of complex underwater scenes and limited target feature extraction ability, the study introduces an efficient correlation channel, i.e., efficient channel attention (ECA), into the YOLOv5 network to enhance the feature extraction ability of the target. Finally, the loss function is improved to enhance the accuracy of the target detection box. Experiments show that the accuracy of the improved YOLOv5 in underwater fish target detection is 2.95% higher than that of the original YOLOv5, and the average detection accuracy (mAP@0.5:0.95) is increased by 5.52%.