In addition to reflecting its own health status, fish behavioral changes are also important in analyzing water quality. The accurate and rapid fish detection is the basis for behavioral change analysis. In order to solve the problem of low precision in the existing multitarget fish detection algorithms, a simple but effective multitarget fish detection algorithm was proposed. A new window scoring strategy was created to generate proposal windows by using the skeleton and edge cues of the fish image. The principal component analysis convolution kernels were trained to extract foreground and background features of fish images. The support vector machine was used to classify proposal windows to obtain windows where fish were located, and the nonmaximum suppression algorithm was used to eliminate redundant windows to complete the object detection. Experiments showed that the proposed algorithm based on the new window scoring strategy had a higher recall rate than the Edge Boxes algorithm, and the recall rate was up to 96.9% under the fixed proposals. The highest classification accuracy of proposal windows can reach 95.71%. By analyzing the missed detection rate, false detection rate and average detection time of the algorithm and Edge Boxes-PCANet, the overall performance of the algorithm was optimal. Using this detection algorithm, the multitarget fish detection can be achieved efficiently and accurately.