Identification of Targeted Regions on an Analogue Site of the Moon by Using Deep Learning Segmentation Algorithm

Lee, K. B.; Shin, Hyu-Soung; Hong, Seunghwan; Park, J. M.

doi:10.1061/9780784483374.096

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…The quality evaluation only considers studies with the “Sum in points” of less than eight as the included study. Therefore, the quality evaluation excludes the studies of references [ 77 , 84 , 85 ].…”

Section: Resultsmentioning

confidence: 99%

Semantic Terrain Segmentation in the Navigation Vision of Planetary Rovers—A Systematic Literature Review

Kuang

Rana

et al. 2022

Sensors

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Background: The planetary rover is an essential platform for planetary exploration. Visual semantic segmentation is significant in the localization, perception, and path planning of the rover autonomy. Recent advances in computer vision and artificial intelligence brought about new opportunities. A systematic literature review (SLR) can help analyze existing solutions, discover available data, and identify potential gaps. Methods: A rigorous SLR has been conducted, and papers are selected from three databases (IEEE Xplore, Web of Science, and Scopus) from the start of records to May 2022. The 320 candidate studies were found by searching with keywords and bool operators, and they address the semantic terrain segmentation in the navigation vision of planetary rovers. Finally, after four rounds of screening, 30 papers were included with robust inclusion and exclusion criteria as well as quality assessment. Results: 30 studies were included for the review, and sub-research areas include navigation (16 studies), geological analysis (7 studies), exploration efficiency (10 studies), and others (3 studies) (overlaps exist). Five distributions are extendedly depicted (time, study type, geographical location, publisher, and experimental setting), which analyzes the included study from the view of community interests, development status, and reimplementation ability. One key research question and six sub-research questions are discussed to evaluate the current achievements and future gaps. Conclusions: Many promising achievements in accuracy, available data, and real-time performance have been promoted by computer vision and artificial intelligence. However, a solution that satisfies pixel-level segmentation, real-time inference time, and onboard hardware does not exist, and an open, pixel-level annotated, and the real-world data-based dataset is not found. As planetary exploration projects progress worldwide, more promising studies will be proposed, and deep learning will bring more opportunities and contributions to future studies. Contributions: This SLR identifies future gaps and challenges by proposing a methodical, replicable, and transparent survey, which is the first review (also the first SLR) for semantic terrain segmentation in the navigation vision of planetary rovers.

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Section: Resultsmentioning

confidence: 99%

Semantic Terrain Segmentation in the Navigation Vision of Planetary Rovers—A Systematic Literature Review

Kuang

Rana

et al. 2022

Sensors

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“…The U‐Net architecture has successfully segmented areas corresponding to landslides, farmland, and clouds in remote‐sensing images [27]. And an R‐CNN based on the ResNet architecture successfully segmented topographic regions from images of a moon replica site [28].…”

Section: Introductionmentioning

confidence: 99%

Automated detection of regions of interest in cartridge case images using deep learning

Le Bouthillier,

Hrynkiw,

Beauchamp

et al. 2023

Journal of Forensic Sciences

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This paper explores a deep‐learning approach to evaluate the position of circular delimiters in cartridge case images. These delimiters define two regions of interest (ROI), corresponding to the breech face and the firing pin impressions, and are placed manually or by an image‐processing algorithm. This positioning bears a significant impact on the performance of the image‐matching algorithms for firearm identification, and an automated evaluation method would be beneficial to any computerized system. Our contribution consists in optimizing and training U‐Net segmentation models from digital images of cartridge cases, intending to locate ROIs automatically. For the experiments, we used high‐resolution 2D images from 1195 samples of cartridge cases fired by different 9MM firearms. Our results show that the segmentation models, trained on augmented data sets, exhibit a performance of 95.6% IoU (Intersection over Union) and 99.3% DC (Dice Coefficient) with a loss of 0.014 for the breech face images; and a performance of 95.9% IoU and 99.5% DC with a loss of 0.011 for the firing pin images. We observed that the natural shapes of predicted circles reduce the performance of segmentation models compared with perfect circles on ground truth masks suggesting that our method provide a more accurate segmentation of the real ROI shape. In practice, we believe that these results could be useful for firearms identification. In future work, the predictions may be used to evaluate the quality of delimiters on specimens in a database, or they could determine the region of interest on a cartridge case image.

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Identification of Targeted Regions on an Analogue Site of the Moon by Using Deep Learning Segmentation Algorithm

Cited by 2 publications

References 4 publications

Semantic Terrain Segmentation in the Navigation Vision of Planetary Rovers—A Systematic Literature Review

Semantic Terrain Segmentation in the Navigation Vision of Planetary Rovers—A Systematic Literature Review

Automated detection of regions of interest in cartridge case images using deep learning

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