Lunar Crater Detection based YoloV5 using CCD Data

Tang, Kexin; Liang, Jiarui; Yan, Pengcheng; Tian, Xiaolin

doi:10.1109/icaica54878.2022.9844588

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…In the end, a total of 83,620 labels were obtained. The number of labels is significantly more than those labelled by Fairweather et al [11] with 43,402, Hashimoto and Mori [24] with 4,967, Yang et al [25] with 14,406, and Lagain et al [26] with 2,142. The number of labels in R1 to R6 are 8,632, 8,857, 23,970, 34,884, 3,519, and 3,758 respectively.…”

Section: Data Preparationmentioning

confidence: 72%

“…Cui et al [9] trained YOLOv5 with SLDEM to detect craters of diameters ranging from 2 to 15 km in the South Pole-Aitken Basin, with accuracy F1 = 95%. Tang et al [11] utilized YOLOv5 to detect kilometer-size craters using Lunar Reconnaissance Orbiter Camera Wide Angle Camera (LROC-WAC) data, with accuracy F1 = 69%. In terms of model construction, most methods directly used the baseline YOLO model to detect craters.…”

Section: Introductionmentioning

confidence: 99%

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YOLO-Crater Model for Small Crater Detection

Mu,

Xian,

et al. 2023

Preprint

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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.

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Section: Data Preparationmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

YOLO-Crater Model for Small Crater Detection

Mu,

Xian,

et al. 2023

Preprint

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show abstract

“…These two networks are classic two-stage object detection models. In addition, we also choose YOLO-v5 network used by [43], which is an advanced onestage object detection model.…”

Section: A Training Details and Comparison Methods 1) Training Detailsmentioning

confidence: 99%

LCDNet: An Innovative Neural Network for Enhanced Lunar Crater Detection Using DEM Data

Miao,

Yan,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Lunar craters are essential for spacecraft landing navigation and lunar exploration missions. Deep learning holds great promise in the crater detection task, but still faces some challenges. One major issue is that using incomplete crater catalogs to create the crater dataset, which negatively impacts the training of deep learning models. Additionally, deep learning models are prone to missing detections in overlapping crater scenes. To address these challenges, we propose a novel Lunar Crater Detection Neural Network (LCD-Net), with two key innovations. The first innovation is the fusion of a Reserved Negative Sampling (RNS) module, which alleviates the problem of incomplete crater annotation in the dataset and detects a large number of potential new craters. The second innovation is the addition of an Adaptive Non-Maximum suppression (A-NMS) module, which optimizes the detection of overlapping craters and reduces the miss rate. We use lunar digital elevation model (DEM) to construct the crater dataset for training and evaluating the detection model. The experimental results demonstrate that LCD-Net achieves recall rates to 96% and 95% on the validation and the test sets, respectively. These results are significantly better than the state-of-the-art methods. Furthermore, LCD-Net also ables to detect tens of thousands of credible new lunar craters that can be used as an expansion to existing crater catalogs, making them more suitable for deep-learning dataset annotation. Overall, our work provides an efficient and reliable solution for lunar crater detection.

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“…Cui et al [18] trained YOLOv5 with SLDEM to detect craters of diameters ranging from 2 to 15 km in the South Pole-Aitken Basin, with an accuracy of F1 = 95%. Tang et al [19] utilized YOLOv5 to detect kilometer-size craters using Lunar Reconnaissance Orbiter Camera Wide Angle Camera (LROC-WAC) data, with an accuracy of F1 = 69%. In terms of model construction, most methods directly use the baseline YOLO model to detect craters.…”

Section: Introductionmentioning

confidence: 99%

YOLO-Crater Model for Small Crater Detection

Mu,

Xian,

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Craters are the most prominent geomorphological features on the surface of celestial bodies, which plays 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. Then, we proposed the YOLO-Crater model for Lunar and Martian small crater detection by replacing EioU and VariFocal loss to solve the crater sample 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 the YOLOX model. 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 strong transferability and generalization capability, which can be applied to detect small craters on the Moon and other celestial bodies.

show abstract

Lunar Crater Detection based YoloV5 using CCD Data

Cited by 4 publications

References 5 publications

YOLO-Crater Model for Small Crater Detection

YOLO-Crater Model for Small Crater Detection

LCDNet: An Innovative Neural Network for Enhanced Lunar Crater Detection Using DEM Data

YOLO-Crater Model for Small Crater Detection

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