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

doi:10.3969/j.issn.1000-1093.2020.10.014

Abstract

Abstract: 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.

Key words: airportrunwaytargetdetection, deeplearning, typicalgeometry, precisecornerregression, lightweightnetwork

CLC Number:

V557⁺.3

LIANG Jie, REN Jun, LI Lei, QI Hang, ZHOU Hongli. Airport Runway Detection Agorithm Based on Accurate Regression of Typical Geometric Shapes[J]. Acta Armamentarii, 2020, 41(10): 2045-2054.

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第41卷第10期2020 年10月
兵工学报ACTA ARMAMENTARII
Vol.41No.10Oct. 2020

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