Aiming at the problem that the regression loss function of IoU boundary box in YOLOv3 algorithm was prone to misidentification and missing identification of smallscale ship prediction box, a new loss function MIoU was proposed by using the normalized distance between the minimum closure area and key points of the target prediction box and the real box, which can significantly improve the regression speed and accuracy of multiscale ship target prediction box. Experiments show that the proposed YOLOv3MIoU algorithm has a recognition accuracy of more than 97% for the six types of ships, and the mAP value reaches 98.44%. Compared with other loss function methods, YOLOv3MIoU has higher recognition accuracy in different scales and types of ship image targets, especially for smallscale ships such as fishing boats, the recognition accuracy is improved by more than 3% higher than other methods, which can meet the application needs of inland shipping safety monitoring.