Teacher-Student Network for Low-quality Remote Sensing Ship Detection

Most CNN-based detectors rely on high-quality images, and the detection performance may be damaged as the image quality decreases. To enhance ship detection for low-quality images, we propose a distillation network which utilize a “teacher” detector with high-quality input images to guide the training of the original “student” detector. In this way, the student detector can learn the advantage information from the teacher and thus achieves improved detection performance. We feed images with different sampling ratios to our network, and the experimental results on HRSC2016 dataset validate the effectiveness of our method. Moreover, we apply our method to different backbones, and the experimental results demonstrate the generality of our method.