Abstract: Aiming at the problems of low detection accuracy and missed detection in real-time detection of ships, this paper improves a ship target detection algorithm based on YOLOV3-Tiny. By introducing depth wise separable convolution as the backbone network, the number of channels was increased and the number of parameters and computation of the model were reduced. The H-Swish and Leaky ReLU activation functions were used to improve the convolution structure in order to extract more feature information. GIOU loss was used to optimize the bounding box to highlight the coincidence degree of the target area and improve the accuracy. The detection results on the mixed ship data set show that the detection accuracy of the improved YOLOv3-Tiny is 83.40%, which is 5.33 percentage points higher than the original algorithm. Its recall rate and detection speed are also better than the original algorithm, which is suitable for real-time ship detection.