Due to the large number of model parameters, common target detection models were often difficult to be deployed on mobile embedded platforms such as unmanned aerial vehicle and satellite. In order to detect ships in real time, and for the purpose of deploying target detection model in weak computing equipment, the ship detection algorithm based on computer vision was researched. According to the feature of ship shape length ratio and width ratio in satellite images, K-means ++ clustering algorithm was used to select the initial candidate anchor boxes. Multi-scale pyramid images were used as the input of model training. The scale factor of the batch normalization layer of the YOLO-v3 target detection algorithm was taken as the measure index of channel importance, and the YOLO-v3 model was pruned and compressed. Experimental results show that model pruning and compression method can effectively compress the model. The number of parameters of the model size is reduced by 91.5% and the time of model detection is shortened by 60% compared with the original model, which greatly reduces the overhead of system computing performance. When the initial number of candidate boxes is 6, the mAP reaches at 77.31%, which meets the requirements of real-time detecting ship in satellite images.