The underwater environment at night has the characteristics of uneven illumination, big noise and low quality of underwater fish video. In view of this, in order to realize the rapid detection of fish targets in underwater images at night, a method of underwater fish object detection at night based on improved Cascade R-CNN algorithm and MSRCP image enhancement algorithm with color protection was proposed by using computer vision technology. Firstly, using the video data of the underwater environment at night, corresponding underwater fish images at night were extracted according to the time interval. The original extracted image was enhanced by MSRCP. Then the DetNASNet backbone network was used to train network model and extract underwater fish feature information. The extracted feature information was input into the Cascade R-CNN model, and the Soft-NMS candidate box optimization algorithm was used to optimize the RPN network. Finally, the underwater fish target at night was detected. The experimental results showed that the method can solve the problems of image degradation and fish object overlapping detection in the underwater environment at night, and realize the rapid detection of underwater fish target at night. Using this method, the accuracy rate of object detection with underwater fish image at night can reach 95.81%, which was 11.57 percentage points higher than that of the traditional Cascade R-CNN method.