Automated Detection of Lunar Craters Using Deep Learning

Jia, Yutong; Wan, Gang; Liu, Lei; Wu, Yitian; Zhang, Chenyang

doi:10.1109/itaic49862.2020.9339179

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Recent studies have demonstrated that by using advancements in computer vision and deep learning, automated crater detection may significantly enhance robustness [19]. CNN was introduced in 2012, and as neural network-based image segmentation methods gained popularity, the relevance evaluation procedures to assess the impact of craters in the Charge Coupled Device (CCD) photos of Chang by Yutong Jia, et al [20]. The SVM classifier utilized the points of interest and relevance of each crater to create a significant feature vector.…”

Section: Related Workmentioning

confidence: 99%

Deep Convolution Neural Network Using TMC-2 DEM Images in Chandrayaan-2

Sinha,

Paul,

Ghosh

et al. 2024

Preprint

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The terrestrial planets, including the Moon, Earth, and Mars, have impact craters, contributing significantly to the solar system's complex geomorphology. However, conventional crater identification methods struggle with the accuracy, of their varied shapes, locations, and sizes. Our main aim is to locate lunar craters using Digital Elevation Model (DEM) images from Terrain Mapping Camera-2 (TMC-2) onboard the Chandrayaan-2 mission. Employing a crater-based U-Net model, CNN, Resnet18, and Image Net are utilized for weight training. The custom semantic segmentation network based on the U-Net model proves effective. The methodology involves Canny Edge Detection, pre-trained models, and bounding boxes for crater localization. Fully Convolutional Neural Networks (FCNN) and U-Net are applied to assess and recognize lunar craters in complex scenarios. The proposed model comprises a neural network, feature extractor, and optimization technique for lunar crater detection. The model achieves 80.95% accuracy using unannotated data and precision and recall are much better with annotated data and accuracy 86.91% in object detection with Chandrayaan-2's DEM photos. As we have only considered 2000 images as annotation is a time-consuming process, in the future we will use more image data sets so that our result is comparatively better for this.

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Section: Related Workmentioning

confidence: 99%

Deep Convolution Neural Network Using TMC-2 DEM Images in Chandrayaan-2

Sinha,

Paul,

Ghosh

et al. 2024

Preprint

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“…As a result of the experiment, 92% of the test data that humans created was restorable [16]. Yutong Jia et al proposed CDA based on U-Nets and achieved crater detection accuracy of 93.4% through repeated training, performed 5000 times [17]. Ali-Dib et al used Mask R-CNN, an "instance segmentation" general framework, to extract 2D shapes in lunar digital elevation maps.…”

Section: Case That Applies Machine Learning Technology To the Moonmentioning

confidence: 99%

“…Lee Honnhee [14] Review Papers (Usually, crater detection) -Jia et al [15] Lunar surface detection Self-calibrated convolution Silburt et al [16] Lunar surface detection CNNs (based U-Net) Yutong Jia et al [17] Lunar surface detection CNNs (based U-Net) Ali-Dib et al [18] Lunar surface detection CNNs (based Mask R-CNN) Shen et al [19] Lunar surface detection High-Resolution-Moon-Net Wilhelm et al [20] Unsupervised learning CNNs (based VGG16) Roy et al [21] Unsupervised learning CNNs (based U-Net) Lesnikowski et al [22] Unsupervised learning CNNs (based VAE) Xia et al [23] Abundance map of oxide and magnesium DNN…”

Section: Research Area Used Modelmentioning

confidence: 99%

Review for Examining the Oxidation Process of the Moon Using Generative Adversarial Networks: Focusing on Landscape of Moon

2022

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Japan Aerospace Exploration Agency (JAXA) has collected and studied the data observed by the lunar probe, SELenological and ENgineering Explorer (SELENE), from 2007 to 2017. JAXA discovered that the oxygen of the upper atmosphere of the Earth is transported to the moon by the tail of the magnetic field. However, this research is still in progress, and more data are needed to clarify the oxidation process. Therefore, this paper supplements the insufficient observation data by using Generative Adversarial Networks (GAN) and proposes a review paper focusing on the methodology, enhancing the level of completion of the preceding research, and the trend of examining the oxidation process and landscape of the moon. We propose using Anokhin’s Conditionally-Independent Pixel Synthesis (CIPS) as a model to be used in future experiments as a result of the review. CIPS can generate pixels independently for each color value, and since it uses a Multi-Layer Perceptron (MLP) network rather than spatial convolutions, there is a significant advantage in scalability. It is concluded that the proposed methodology will save time and costs of the existing research in progress and will help reveal the causal relationship more clearly.

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“…Lunar topography includes several features including rocks, boulders and craters, while the terrain in many areas is quite uneven with mounds and valleys. Although several studies propose methodologies for crater [40][41][42] or hazard [43] detection and segmentation, they focus on safe landing using remote-sensing images while there is a deficiency in rock and boulder identification during the rover navigation; a quite important issue for the smooth and trouble-free navigation.…”

Section: Introductionmentioning

confidence: 99%

Lunar ground segmentation using a modified U-net neural network

Petrakis,

Partsinevelos

2024

Machine Vision and Applications

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Semantic segmentation plays a significant role in unstructured and planetary scene understanding, offering to a robotic system or a planetary rover valuable knowledge about its surroundings. Several studies investigate rover-based scene recognition planetary-like environments but there is a lack of a semantic segmentation architecture, focused on computing systems with low resources and tested on the lunar surface. In this study, a lightweight encoder-decoder neural network (NN) architecture is proposed for rover-based ground segmentation on the lunar surface. The proposed architecture is composed by a modified MobilenetV2 as encoder and a lightweight U-net decoder while the training and evaluation process were conducted using a publicly available synthetic dataset with lunar landscape images. The proposed model provides robust segmentation results, allowing the lunar scene understanding focused on rocks and boulders. It achieves similar accuracy, compared with original U-net and U-net-based architectures which are 110–140 times larger than the proposed architecture. This study, aims to contribute in lunar landscape segmentation utilizing deep learning techniques, while it proves a great potential in autonomous lunar navigation ensuring a safer and smoother navigation on the moon. To the best of our knowledge, this is the first study which propose a lightweight semantic segmentation architecture for the lunar surface, aiming to reinforce the autonomous rover navigation.

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Automated Detection of Lunar Craters Using Deep Learning

Cited by 8 publications

References 7 publications

Deep Convolution Neural Network Using TMC-2 DEM Images in Chandrayaan-2

Deep Convolution Neural Network Using TMC-2 DEM Images in Chandrayaan-2

Review for Examining the Oxidation Process of the Moon Using Generative Adversarial Networks: Focusing on Landscape of Moon

Lunar ground segmentation using a modified U-net neural network

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