Ship detection based on the modified YOLOV5 for SAR imagery

Synthetic aperture radar (SAR) is an essential tool for ocean surveillance. As the main participants on the ocean, ships are the most important targets for ocean monitoring. So it is of great importance to develop ship detection algorithms for SAR sea images. The algorithms based on Convolutional Neural Network perform far better than the traditional methods based on manual features on ship detection task due to the powerful feature representation abilities. The algorithms based on Convolutional Neural Network can be divided into one-stage algorithms, and two-stage algorithms. Two-stage algorithms have high accuracy, but are relatively time-consuming. One-stage algorithms have high inference speed, but compared with two-stage algorithms, they have lower accuracy. So in this article, we proposed an modified one-stage detection algorithm to improve the accuracy of ship detection in a condition that the modified algorithm meet the real-time requirement. First, the small model of one-stage algorithm YOLOV5 is chosen as the base network to get the high inference speed. Then, to improve the accuracy of ship detection with a little increase in inference time and the model parameters, we integrate the one–layer super-resolution architecture with the simplist structure into the YOLOV5 network. Finally, we conducted the comparative experiments on our Dataset to verify the performance of modified YOLO V5. The experimental results show that the modified method has obtained an Average Precision (AP) improvement than the original YOLO V5 for detecting ships in SAR images with a little increase in inference time and the model parameters.