Deep Learning based Systems for Crater Detection: A Review
Atal Tewari K Prateek Amrita Singh Nitin Khanna
Craters are one of the most prominent features on planetary surfaces, used in
applications such as age estimation, hazard detection, and spacecraft
navigation. Crater detection is a challenging problem due to various aspects,
including complex crater characteristics such as varying sizes and shapes, data
resolution, and planetary data types. Similar to other computer vision tasks,
deep learning-based approaches have significantly impacted research on crater
detection in recent years. This survey aims to assist researchers in this field
by examining the development of deep learning-based crater detection algorithms
(CDAs). The review includes over 140 research works covering diverse crater
detection approaches, including planetary data, craters database, and
evaluation metrics. To be specific, we discuss the challenges in crater
detection due to the complex properties of the craters and survey the DL-based
CDAs by categorizing them into three parts: (a) semantic segmentation-based,
(b) object detection-based, and (c) classification-based. Additionally, we have
conducted training and testing of all the semantic segmentation-based CDAs on a
common dataset to evaluate the effectiveness of each architecture for crater
detection and its potential applications. Finally, we have provided
recommendations for potential future works.