Deep Convolutional Neural Network Based Small Space Debris Saliency Detection

Jiang, Tao; Cao, Yunfeng; Zhuang, Likui; Zhang, Zhouyu; Ding, Meng

doi:10.23919/iconac.2019.8895100

Cited by 11 publications

(6 citation statements)

References 15 publications

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“…In ref. [22], the authors present a novel approach using a fully convolutional network to detect the salience of space debris in a space surveillance platform. Unlike traditional methods that rely on costly optical flow calculations, this network learns the internal relationship between consecutive frames directly.…”

Section: Related Workmentioning

confidence: 99%

Deep learning‐based space debris detection for space situational awareness: A feasibility study applied to the radar processing

Massimi,

Ferrara,

Petrucci

et al. 2024

IET Radar Sonar & Navi

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The increasing number of space objects (SO), debris, and constellation of satellites in Low Earth Orbit poses a significant threat to the sustainability and safety of space operations, which must be carefully and efficiently addressed to avoid mutual collisions. The space situational awareness is currently addressed by an ensemble of radar and radio‐telescopes that detect and track SO. However, a large part of space debris is composed of very small and tiny metallic objects, very difficult to detect. The authors demonstrate the benefits of using deep learning (DL) architectures for small space object detection by radar observations. TIRA radio telescope has been simulated to generate range‐Doppler maps, then used as inputs for object detection exploiting You‐Only‐Look‐Once (YOLO) frameworks. The results demonstrate that the object detection by using YOLO algorithms outperform conventional target detection approaches, thus indicating the potential benefits of using DL techniques for space surveillance applications.

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Section: Related Workmentioning

confidence: 99%

Deep learning‐based space debris detection for space situational awareness: A feasibility study applied to the radar processing

Massimi,

Ferrara,

Petrucci

et al. 2024

IET Radar Sonar & Navi

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“…Effective and accurate monitoring of abnormal power usage, utilizing abnormal power information obtained from the user side of the smart grid, is of paramount importance. Tao H. [6] and other researchers utilized energy consumption data from equipment to derive classification rules for users' normal and abnormal power usage models. They proposed an abnormal detection method for household power consumption based on a convolutional neural network.…”

Section: Related Workmentioning

confidence: 99%

An anomaly Detection Method for Electricity Consumption Data Based on CNN-BiLSTM-Attention

Junwei Zhang, Xiaofang Chen

2024

jes

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As the complexity and uncertainty of smart distribution networks increase, data security issues in smart meters have become a pressing challenge, such as false data injection and electricity theft. To ensure fairness, safety, and overall economic efficiency of distribution networks, it is essential to accurately detect abnormal electricity consumption. However, traditional methods relying on on-site inspections by grid personnel suffer from low efficiency and high costs in detecting user anomalies. This paper proposes an electricity consumption data anomaly detection method based on CNN-BiLSTM-Attention. CNN is utilized to extract data features, while BiLSTM and attention mechanisms capture contextual information in sequence data. Furthermore, experiments conducted on data extracted from smart meters demonstrate that the proposed model outperforms other models in anomaly detection, with accuracy, recall, and F1-Score all exceeding 91%. These results validate the effectiveness and feasibility of the proposed method, providing an efficient solution for user anomaly detection in national power grids.

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“…It has good performance and can detect targets with low SNR, but the probability of detection can still be improved. Jiang et al [15] proposed a CNN framework depending on a small SD detection that had two processes. In the first process, the astronomical image's spatial contrast map (SCM) is produced using the local-contrast technique.…”

Section: Deep Learning-based Streak Detectionmentioning

confidence: 99%

Streak detection in astronomical images based on convolutional neural network

Elhakiem,

Ghoniemy,

Salama

2023

J. Phys.: Conf. Ser.

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Streak detection is important in space situational awareness and space asset protection. It is desirable to detect moving targets (e.g., satellites, space debris, or meteorites) in images of the sky. This paper presents a comparison between two astronomical frameworks for streak detection based on deep CNNs. The first framework uses the extended feature pyramid network (EFPN) with faster region-based CNNs (Faster R-CNN) and compares it with the second framework that uses the feature pyramid network (FPN) with Faster R-CNN. Because there aren’t enough publicly available astronomical data sets, we use the simulated data set to train the neural network. The metrics of mean average precision (mAP), recall, precision, and F1 score were used to measure the performance of the two frameworks. The experimental results confirmed that the EFPN-based framework achieves a significant improvement in streak detection than the Faster R-CNN framework based on the FPN model.

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Deep Convolutional Neural Network Based Small Space Debris Saliency Detection

Cited by 11 publications

References 15 publications

Deep learning‐based space debris detection for space situational awareness: A feasibility study applied to the radar processing

Deep learning‐based space debris detection for space situational awareness: A feasibility study applied to the radar processing

An anomaly Detection Method for Electricity Consumption Data Based on CNN-BiLSTM-Attention

Streak detection in astronomical images based on convolutional neural network

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