Article
Version 1
Preserved in Portico This version is not peer-reviewed
YOLO-Crater Model for Small Crater Detection
Version 1
: Received: 31 August 2023 / Approved: 1 September 2023 / Online: 4 September 2023 (03:58:47 CEST)
A peer-reviewed article of this Preprint also exists.
Mu, L.; Xian, L.; Li, L.; Liu, G.; Chen, M.; Zhang, W. YOLO-Crater Model for Small Crater Detection. Remote Sens. 2023, 15, 5040. Mu, L.; Xian, L.; Li, L.; Liu, G.; Chen, M.; Zhang, W. YOLO-Crater Model for Small Crater Detection. Remote Sens. 2023, 15, 5040.
Abstract
Craters are the most prominent geomorphological features on the surface of celestial bodies, which is playing a crucial role in studying the formation and evolution of celestial bodies, as well as in landing and planning for surface exploration. Currently, the main automatic crater detection models and datasets focus on the detection of large and medium craters. In this paper, we created 23 small lunar crater datasets for model training based on the Chang’E-2 (CE-2) DOM, DEM, Slope, and integrated data with 7 kinds of visualization stretching methods. And then, we proposed the YOLO-Crater model for Lunar and Martian small crater detection by replacing EioU and VariFocal loss to solve the crater samples imbalance problem and introducing a CBAM attention mechanism to mitigate interference from the complex extraterrestrial environment. The results show that the accuracy (P = 87.86%, R = 66.04%, and F1 = 75.41%) of the Lunar YOLO-Crater model based on the DOM-MMS (Maximum-Minimum Stretching) dataset is the highest and better than that of YOLOX model. And the Martian YOLO-Crater, trained by the Martian dataset from the 2022 GeoAI Martian Challenge, achieves good performance with P = 88.37%, R = 69.25%, and F1 = 77.65%. It indicates that the YOLO-Crater model has a strong transferability and generalization capability, which can be applied to detect small craters on the Moon and other celestial bodies.
Keywords
small crater detection; YOLO-Crater; Efficient-IoU (EIoU); VariFocal; Convolutional Block Attention Module (CBAM); DOM; DEM; Slope; stretching method
Subject
Environmental and Earth Sciences, Space and Planetary Science
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
