Airport Runway Detection Agorithm Based on Accurate Regression of Typical Geometric Shapes

被引：0
|
作者：
Liang J. [1 ]
Ren J. [1 ]
Li L. [1 ,2 ]
Qi H. [1 ]
Zhou H. [1 ]
机构：
[1] Beijing Institute of Mechanical and Electrical Engineering, Beijing
[2] Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory, Beijing
来源：
Li, Lei (univer1@sina.com) | 1600年 / China Ordnance Industry Corporation卷 / 41期
关键词：
Airport runway target detection; Deep learning; Lightweight network; Precise corner regression; Typical geometry;
D O I：
10.3969/j.issn.1000-1093.2020.10.014
中图分类号：
学科分类号：
摘要：
In the field of remote sensing detection, it is of great significance to achieve accurate detection of ground runway targets and contours under complex environmental conditions. The mainstream deep learning algorithm represented by YOLOv3 has achieved remarkable results in the field of target detection, but this algorithm can only give the approximate position of target in a rectangular frame, the detection result has a certain background area and cannot accurately get corner position. For the above problems, an airport runway detection algorithm based on the exact regression of typical geometric shape is proposed. Through the utilization of the typical quadrilateral corner regression strategy, the quadrilateral anchor frame mechanism, the quadrilateral non-maximum suppression module, the target geometric topological relationship, and the lightweight design of the network and model compression, the proposed algorithm can realize to learn the imaging characteristics of target under affine distortion, quickly predict the corner coordinates of target, and finally give its position with the quadrilateral contour of target. Experimental results show that the proposed algorithm has the functions of airport runway target type discrimination and contour extraction, which effectively solves the problem of accurate target positioning in practical applications, and doubles the detection speed without losing accuracy, and greatly improve the accuracy and efficiency of automatic target recognition. © 2020, Editorial Board of Acta Armamentarii. All right reserved.
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页码：2045 / 2054
页数：9
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