Vision-based crater and rock detection using a cascade decision forest

Both crater and rock detection are components of the autonomous landing and hazard avoidance technology (ALHAT) sensor suite, as craters and rocks represent the majority of landing hazards. Furthermore, places with scientific values are very probable next to craters and rocks. Unsupervised approaches, which potentially use the pattern recognition techniques of ring threshold finding, perform quickly; however, they suffer from handling small craters. The supervised pattern recognition method is more powerful but is time-consuming. To address these issues, here, a simultaneous multi-size crater and rock detection algorithm is studied. The authors propose a new supervised machine-learning framework using a cascade decision forest. Sliding windows are utilised in order to search basic features, and a multi-grained cascade structure is introduced to enhance the framework's ability to learn the representations of the features. The training time of the proposed algorithm on a PC is comparable to that of deep neural networks, and the efficiency is enhanced for a large-scale database. The outputs of the simulation verify the effectiveness and validity of the introduced technique.