Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data

Zotov, Mikhail; Anzhiganov, Dmitry; Kryazhenkov, Aleksandr; Barghini, Dario; Battisti, Matteo; Belov, Alexander; Bertaina, Mario; Bianciotto, Marta; Bisconti, Francesca; Blaksley, Carl; Blin, Sylvie; Cambiè, Giorgio; Capel, Francesca; Casolino, Marco; Ebisuzaki, Toshikazu; Eser, Johannes; Fenu, Francesco; Franceschi, Massimo; Golzio, Alessio; Gorodetzky, Philippe; Kajino, Fumiyoshi; Kasuga, Hiroshi; Klimov, Pavel; Manfrin, Massimiliano; Marcelli, Laura; Miyamoto, Hiroko; Murashov, Alexey; Napolitano, Tommaso; Ohmori, Hiroshi; Olinto, Angela; Parizot, Etienne; Picozza, Piergiorgio; Piotrowski, Lech; Plebaniak, Zbigniew; Prévôt, Guillaume; Reali, Enzo; Ricci, Marco; Romoli, Giulia; Sakaki, Naoto; Shinozaki, Kenji; De La Taille, Christophe; Takizawa, Yoshiyuki; Vrábel, Michal; Wiencke, Lawrence

doi:10.3390/a16090448

Cited by 2 publications

(4 citation statements)

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“…We will refer to it by the acronym CNN + MLP. See [21] for more details. The overall structure is extremely similar to our method, as both algorithms implement CNNs to classify images, and then a light-curve classifier is used to suppress false positives.…”

Section: B Ablation Studymentioning

confidence: 99%

“…An example is the orbital detector TUS (Tracking Ultraviolet Setup), onboard the Lomonosov Satellite [20], which showed promising results in meteor detection from space images. In parallel to this work, a new approach using a CNN and a fully-connected network [21] is being investigated to find new meteors in some sessions of the Mini-EUSO data. Their approach still implements a CNN to select meteor images, and then a fully connected layer to classify pixels of such image containing meteor events.…”

Section: Introductionmentioning

confidence: 99%

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Refined STACK-CNN for Meteor and Space Debris Detection in Highly Variable Backgrounds

Olivi,

Montanaro,

Bertaina

et al. 2024

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

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In this work we present cutting-edge machine learning based techniques for the detection and reconstruction of meteors and space debris in the Mini-EUSO experiment, a detector installed on board of the International Space Station (ISS), and pointing towards the Earth. We base our approach on a recent technique, the Stack-CNN, originally developed as an online trigger in a orbiting remediation system to detect space debris. Our proposed method, the Refined Stack-CNN (R-Stack-CNN), makes the STACK-CNN more robust thanks to a Random Forest (RF) that learns the temporal development of these events in the camera. We prove the flexibility of our method by showing that it is sensitive to any space object that moves linearly in the field of view. First, we search small space debris, never observed by Mini-EUSO. Due to the limiting statistics, also in this case no debris were found. However, since meteors produce signals similar to space debris but they are much more frequent, the R-Stack-CNN is adapted to identify such events while avoiding the numerous false positives of the Stack-CNN. Results from real data show that the R-Stack-CNN is able to find more meteors than a classical thresholding method and a new method of two neural networks. We also show that the method is also able to accurately reconstruct speed and direction of meteors with simulated data.

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Section: B Ablation Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Refined STACK-CNN for Meteor and Space Debris Detection in Highly Variable Backgrounds

Olivi,

Montanaro,

Bertaina

et al. 2024

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

Self Cite

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show abstract

“…Target detection is the basis of target tracking [1] and intelligent vehicle obstacle avoidance [2]. In addition, target detection has been widely used in areas such as military defense and security surveillance [3][4][5]. Pedestrian detection is designed to accurately recognize and detect pedestrians in various scenes [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…To reduce the loss of the network, we use the WIoU loss function in the head to reduce the regression loss of the boundary box. 4.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Detection Based on Feature Enhancement in Complex Scenes

Su,

An,

et al. 2024

Algorithms

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Pedestrian detection has always been a difficult and hot spot in computer vision research. At the same time, pedestrian detection technology plays an important role in many applications, such as intelligent transportation and security monitoring. In complex scenes, pedestrian detection often faces some challenges, such as low detection accuracy and misdetection due to small target sizes and scale variations. To solve these problems, this paper proposes a pedestrian detection network PT-YOLO based on the YOLOv5. The pedestrian detection network PT-YOLO consists of the YOLOv5 network, the squeeze-and-excitation module (SE), the weighted bi-directional feature pyramid module (BiFPN), the coordinate convolution (coordconv) module and the wise intersection over union loss function (WIoU). The SE module in the backbone allows it to focus on the important features of pedestrians and improves accuracy. The weighted BiFPN module enhances the fusion of multi-scale pedestrian features and information transfer, which can improve fusion efficiency. The prediction head design uses the WIoU loss function to reduce the regression error. The coordconv module allows the network to better perceive the location information in the feature map. The experimental results show that the pedestrian detection network PT-YOLO is more accurate compared with other target detection methods in pedestrian detection and can effectively accomplish the task of pedestrian detection in complex scenes.

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Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data

Cited by 2 publications

References 32 publications

Refined STACK-CNN for Meteor and Space Debris Detection in Highly Variable Backgrounds

Refined STACK-CNN for Meteor and Space Debris Detection in Highly Variable Backgrounds

Pedestrian Detection Based on Feature Enhancement in Complex Scenes

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