Progressive Domain Adaptive Network for Crater Detection

Existing crater detection methods are typically carried out in the same domain where the training and testing data are drawn from an identical distribution. However, this means that when we apply a trained crater detector to a new scene probably caused by changes in sensors, lighting or other factors will lead to a significant performance drop. In this work, we aim to improve the accuracy of the domain (scene) adaptive crater detection, which will reduce labor costs caused by annotation. First, we proposed a progressive domain adaptive network (PDAN), which can progressively learn the knowledge from the source domain to the target domain by the projected intermediate domain features generated by the subspace along the geodesic on the Grassmann manifold. Second, in order to further extract the domain-agnostic feature and utilize the characteristics of craters, we proposed a low-level feature enhancement module (LFEM) and a circular boundary enhancement module (CBEM). Third, a weighted instance-level alignment network (WIAN) is presented to adaptively align the instance-level feature, further reduce the differences between two domains, and enhance the performance. To evaluate the effectiveness of the proposed method, based on our previous Mars day crater dataset (MDCD), we present a domain adaptive crater detection (DACD) dataset, which contains 1000 images in two domains and more than 20000 craters. The extensive experiments based on the DACD and open datasets demonstrated that the proposed network could effectively eliminate the impact of domain shift and achieve state-of-the-art results.