A combined method of crater detection and recognition based on deep learning

Abstract

The crater is one of the main obstacles that need to be avoided when Mars probe lands. In order to further improve the accuracy of crater detection, this paper proposes a combined detection method based on deep learning. Firstly, the random structured forest is trained offline to detect the edge information of craters. Secondly, according to the detected edge information of the crater, the candidate areas of the crater are determined with the morphological method. For the identified candidate areas of the crater, Alexnet network trained by transfer learning was used to identify crater areas. Compared with other methods, the proposed method has relatively good effect.