Hard Ship Detection via Generative Adversarial Networks

In optical remote sensing images, many ships have very similar shapes and textures with backgrounds. In this case, it is very hard to accurately detect these ships. In this paper, we introduce generative adversarial networks (GANs) to perform hard ship detection. GANs consist of one generative network and one discriminator network. We take state-of-the-art object (ship) detection network Faster R-CNN as the generative network, which outputs the detection results as fake samples. The ground-truth ships in the input image are set as the real samples. The discriminator network is responsible for distinguishing between fake samples and real samples. The two networks are simultaneously trained. Through continuous adversarial training, the fake samples generated by the generative network can be very similar to the real samples, and the discriminator network would not correctly distinguish between fake samples and real samples. As a result, the ship detection network (generative network) correctly recognizes hard-detection ships, producing satisfactory detection results. What's more, the discriminator network is only used in training process, and thus the proposed method not only improves detection accuracy, but also does not increase computational cost.