Ship target detection method based on improved CenterNet in synthetic aperture radar images

Deep learning has been widely used for the ship target detection in the synthetic aperture radar (SAR) images. The existing researches mainly uses the anchor frame-based detection method to generate the candidate frames to extract the specific targets. However, this method requires the additional computing resources to filter out the many repeated candidate frames, which will lead to the poor target positioning accuracy and low detection efficiency. To solve these problems, this paper constructs an anchor-free frame for the ship target detection in the SAR images. An improved lightweight detection method based on the target key point is proposed for the real-time detection of the SAR images, which can achieve the rapid and accurate positioning of the ship targets in the SAR images. The experimental results prove that the proposed method has the better detection performance and stronger generalization capability, which is beneficial to realize the real-time detection of the ship targets.