An Improved Deep Keypoint Detection Network for Space Targets Pose Estimation

Xu, Junjie; Song, Bin; Yang, Xi; Nan, Xiaoting

doi:10.3390/rs12233857

Cited by 13 publications

(7 citation statements)

References 46 publications

(62 reference statements)

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“…The reason for choosing HRNet [25] is that the high-resolution representation is maintained in the whole pipeline of the network, which enables the model to extract features with superior spatial relationships that are suitable for localization-related tasks. Based on [8], Xu et al [9] applied dilated convolutions to fuse multi-scale features in the HRNet [25] to better mine global information, and presented an online hard landmark mining method to enhance the ability of the network to detect invisible landmarks. In addition, Wang et al [26] proposed a set-based representation to fully explore the relationship among keypoints and the context between the keypoints and the satellite.…”

Section: Landmark-based Methodsmentioning

confidence: 99%

“…Park et al [7] are the first to propose using DCNNs to predict the 2D coordinates of pre-defined landmarks of the space target to build 2D-3D correspondences. Based on the pipeline as described in [7], several improved methods [8][9][10] were proposed to enhance the performance of landmark regression. In particular, the 2D heatmap representation plays an important role in regressing landmarks and significantly promotes the improvement of landmark regression performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep 1D Landmark Representation Learning for Space Target Pose Estimation

Liu¹,

Zhu²,

Cao³

et al. 2022

Remote Sensing

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Monocular vision-based pose estimation for known uncooperative space targets plays an increasingly important role in on-orbit operations. The existing state-of-the-art methods of space target pose estimation build the 2D-3D correspondences to recover the space target pose, where space target landmark regression is a key component of the methods. The 2D heatmap representation is the dominant descriptor in landmark regression. However, its quantization error grows dramatically under low-resolution input conditions, and extra post-processing is usually needed to compute the accurate 2D pixel coordinates of landmarks from heatmaps. To overcome the aforementioned problems, we propose a novel 1D landmark representation that encodes the horizontal and vertical pixel coordinates of a landmark as two independent 1D vectors. Furthermore, we also propose a space target landmark regression network to regress the locations of landmarks in the image using 1D landmark representations. Comprehensive experiments conducted on the SPEED dataset show that the proposed 1D landmark representation helps the proposed space target landmark regression network outperform existing state-of-the-art methods at various input resolutions, especially at low resolutions. Based on the 2D landmarks predicted by the proposed space target landmark regression network, the error of space target pose estimation is also smaller than existing state-of-the-art methods under all input resolution conditions.

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Section: Landmark-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep 1D Landmark Representation Learning for Space Target Pose Estimation

Liu¹,

Zhu²,

Cao³

et al. 2022

Remote Sensing

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“…Xu et al [31] integrated extended convolutional high-resolution networks with online hard key point mining strategies to address imaging challenges in intricate space environments. Their enhanced network prioritizes occluded key points, extends the receptive field, and improves detection accuracy.…”

Section: Satellite Target Detection and Recognition Algorithmsmentioning

confidence: 99%

TYCOS: A Specialized Dataset for Typical Components of Satellites

Bian,

Cao,

Zhang

et al. 2024

Applied Sciences

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The successful detection of key components within satellites is a crucial prerequisite for executing on-orbit capture missions. Due to the inherent data-driven functionality, deep learning-based component detection algorithms rely heavily on the scale and quality of the dataset for their accuracy and robustness. Nevertheless, existing satellite image datasets exhibit several deficiencies, such as the lack of satellite motion states, extreme illuminations, or occlusion of critical components, which severely hinder the performance of detection algorithms. In this work, we bridge the gap via the release of a novel dataset tailored for the detection of key components of satellites. Unlike the conventional datasets composed of synthetic images, the proposed Typical Components of Satellites (TYCOS) dataset comprises authentic photos captured in a simulated space environment. It encompasses three types of satellite, three types of key components, three types of illumination, and three types of motion state. Meanwhile, scenarios with occlusion in front of the satellite are also taken into consideration. On the basis of TYCOS, several state-of-the-art detection methods are employed in rigorous experiments followed by a comprehensive analysis, which further enhances the development of space scene perception and satellite safety.

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“…Benbihi et al [152] applied a simple method in which the gradient responses of deep learning features are used for detecting interest points. Xu et al [160] applied dilated convolution to obtain a feature response map with multi-resolution for improving the scale invariant property of the detection method and solve the problem of detecting occluded interest points by adding tags of occluded interest points in the training set.…”

Section: Machine Learning Based Ifi Extraction Techniques For Interes...mentioning

confidence: 99%

Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review

Gao

Zhang

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image feature information (IFI) extraction techniques for interest point detection. To systematically introduce how the existing interest point detection methods extract IFI from an input image, we propose a taxonomy of the IFI extraction techniques for interest point detection. According to this taxonomy, we discuss different types of IFI extraction techniques for interest point detection. Furthermore, we identify the main unresolved issues related to the existing IFI extraction techniques for interest point detection and any interest point detection methods that have not been discussed before. The existing popular datasets and evaluation standards are provided and the performances for fifteen state-of-the-art approaches are evaluated and discussed. Moreover, future research directions on IFI extraction techniques for interest point detection are elaborated.

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An Improved Deep Keypoint Detection Network for Space Targets Pose Estimation

Cited by 13 publications

References 46 publications

Deep 1D Landmark Representation Learning for Space Target Pose Estimation

Deep 1D Landmark Representation Learning for Space Target Pose Estimation

TYCOS: A Specialized Dataset for Typical Components of Satellites

Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review

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