Target Recognition Method of Laser Line Scanning Imaging Fuze Based on DHGF Algorithm

Zheng, Zhen; Bingting, 查冰婷 Zha; He, 张合 Zhang

doi:10.3788/cjl201845.0704004

Cited by 3 publications

(2 citation statements)

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“…Using the method in reference [10] for target recognition, a normalized comprehensive evaluation result was obtained:…”

Section: Target Recognitionmentioning

confidence: 99%

Target recognition algorithm based on sparse LiDAR scanning data

Zhao,

Wang,

et al. 2024

International Conference on Optoelectronic Information and Functional Materials (OIFM 2024)

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For target recognition of sparse LiDAR scan data, this paper proposes a new method for aligning height data matrices based on rotation, length segmentation, and grid partitioning. Initially, the method involves rotating the matrix to achieve initial pose alignment between the height matrix of the target to be recognized and the template matrix. Subsequently, utilizing length segmentation, the orientation of the target is aligned, and the initial grid search position is determined. Finally, the method searches for the best alignment grid based on grid partitioning to achieve data matrix alignment and performs target recognition using a fuzzy recognition algorithm based on scanlines. Experimental results demonstrate that this method effectively aligns sparse target data with target template data, also enabling the recognition of typical targets such as cars, providing a valuable reference for target recognition of sparse LiDAR scan data.

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“…Using the method in reference [10] for target recognition, a normalized comprehensive evaluation result was obtained:…”

Section: Target Recognitionmentioning

confidence: 99%

Target recognition algorithm based on sparse LiDAR scanning data

Zhao,

Wang,

et al. 2024

International Conference on Optoelectronic Information and Functional Materials (OIFM 2024)

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show abstract

“…The 2D image of the target obtained by the traditional visible-light imaging technology can only provide a small amount of target information [1], which is difficult to meet the needs in many cases, while the point cloud data obtained by the 3D imaging technology based on laser scanning can provide more target information, such as spatial coordinates, color, intensity et al [2][3][4]. so the target recognition problem based on point cloud data has received extensive attention [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Point Cloud Target Registration Algorithm Based on Local Feature Description

Wang

Zha

2023

J. Phys.: Conf. Ser.

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Point cloud data has received extensive attention in the field of target recognition and target tracking due to its rich target information, and point cloud registration is one of the basic tasks in point cloud applications. Aiming at the problem that the feature descriptor in the traditional registration algorithm is complicated to calculate and cannot sufficiently describe the features of the point cloud, this paper proposes a local feature descriptor that integrates the local normal vector angle features, projection distance features, and distribution density features of the point cloud. The feature similarity is introduced to determine the corresponding point pairs between the template point cloud and the target point cloud, and the point cloud registration task is completed based on local feature matching. Experiments show that the feature descriptor in this paper is simple to calculate and can sufficiently describe the local features of the point cloud, a good correspondence between points can be established based on the feature descriptor, and the algorithm has good robustness. The registration algorithm proposed in this paper can provide a reference for the fields of target recognition and target pose estimation, etc.

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Development and prospect of advanced fuze technology

Zhang¹,

Dai²

2023

Chin. Sci. Bull.

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Target Recognition Method of Laser Line Scanning Imaging Fuze Based on DHGF Algorithm

Cited by 3 publications

References 0 publications

Target recognition algorithm based on sparse LiDAR scanning data

Target recognition algorithm based on sparse LiDAR scanning data

Point Cloud Target Registration Algorithm Based on Local Feature Description

Development and prospect of advanced fuze technology

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