| Citation: | ZHENG Lei, HU Weiduo, LIU Changet al. Large crater identification method based on deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(5): 994-1004. doi: 10.13700/j.bh.1001-5965.2019.0342(in Chinese)
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Craters are the most significant topographic features on the surface of celestial bodies. The traditional method of craters identification is mainly to study the dichotomy of positive and negative samples of small craters, with low efficiency and accuracy. This paper takes large craters under the macroscopic view of the planet as the research object, combines the knowledge of digital image processing and neural network, creates a crater sample library of different data sources to study the influence of data source on network model generalization ability, and proposes a more efficient crater multi-classification identification method. Based on the Non-Maximum Suppression (NMS) algorithm, a higher precision crater detection algorithm is proposed. Through parameter optimization and experimental verification, the multi-scale and multi-classification craters automatic recognition network framework based on deep learning constructed in this paper achieves a high accuracy rate, with the recognition rate up to 0.985 on homologous verification set and 0.863 on heterogeneous verification set, and effectively improves the redundancy of detection box and false detection in target detection.
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ZHENG Lei, HU Weiduo, LIU Changet al. Large crater identification method based on deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(5): 994-1004. doi: 10.13700/j.bh.1001-5965.2019.0342(in Chinese)
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