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Target-oriented deformable fast depth estimation based on stereo vision for space object detection
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DOI：10.1016/j.measurement.2024.116621.png)
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摘要
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To address problems of the poor matching accuracy and speed in space object detection, this paper proposes a deformable fast object matching algorithm. It is a target-oriented depth estimation approach that calculates the disparity value by matching the object on the left and right images. The logical encoding masking layer is designed to achieve the deformable operation, which can fully fuse the semantic or contour feature information of the object. This effectively reduces the computational cost and improves the accuracy. The feature coding method is optimized and upgraded by integrating relative positions and global pixels in the image, solving the problem of mismatching in complex regions without obvious features. Based on the characteristics of stereo vision and the concept of regional matching, an optimal matching search range is proposed. Results show that the average time is less than 9ms and accuracy reaches the state-of-the-art.
Keyword:
Intelligent vehicles
Space object detection
Object matching
Depth estimation
期刊
IF:
5.6
论文数:
2.0W
被引数:
5.4W
机构
引用论文
Swin-Depth: Using Transformers and Multi-Scale Fusion for Monocular-Based Depth Estimation.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
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