Ship detector in SAR images based on EfficientDet without pre-training

Aiming at the problem of multi-scale and multi-scene Synthetic Aperture Radar (SAR) ship detection, an object detector without pre-training based on EfficientDet is proposed. The existing SAR image ship detectors based on convolutional neural networks do not show excellent performance that it should have. One of the important reasons is that they depend on the pre-training model of the classification tasks, and there is no effective method to solve the difference between the SAR image and the natural scene image. Another important reason is that the information of each layer of the convolutional network is not fully utilized, the feature fusion ability is not strong enough to deal with the detection of ships in multiple scenes including sea and offshore, and especially the interference of complex offshore background cannot be ruled out. SED improves the method in these two aspects, and conducts experiments on the public SAR ship detection data set. The detection accuracy index AP of SED reaches 94.2%, which, compared with the classic deep learning detector, has exceeded the best RetineNet model by 1.3%, and achieved a balance among model size, computing power consumption and detection speed. This verifies that the model can achieve excellent performance in multi-scale SAR image ship detection in multiple scenes. © 2021, Editorial Board of JBUAA. All right reserved.