首页|Drogue detection for autonomous aerial refueling via hybrid pigeon-inspired optimized color opponent and saliency aggregation

Drogue detection for autonomous aerial refueling via hybrid pigeon-inspired optimized color opponent and saliency aggregation

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Drogue detection is one of the challenging tasks in autonomous aerial refueling due to the requirement for accuracy and rapidity.Saliency detection based on image intrinsic cues can achieve fast detection,but with poor accuracy.Recent studies reveal that optimization-based meth-ods provide accurate and quick solutions for saliency detection.This paper presents a hybrid pigeon-inspired optimization method,the optimized color opponent,that aims to adjust the weight of color opponent channels to detect the drogue region.It can optimize the weights in the selected aerial refueling scene offline,and the results are applied for drogue detection in the scene.A novel algorithm aggregated by the optimized color opponent and robust background detection is pre-sented to provide better precision and robustness.Experimental results on benchmark datasets and aerial refueling images show that the proposed method successfully extracts the saliency region or drogue and exhibits superior performance against the other saliency detection methods with intrinsic cues.The algorithm designed in this paper is competent for the drogue detection task of autonomous aerial refueling.

Autonomous aerial refuelingDronesHybrid pigeon-inspired opti-mizationColor opponentSaliency detectionSaliency aggregation

Tongyan WU、Haibin DUAN、Yanming FAN

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State Key Laboratory of Virtual Reality Technology and Systems,School of Automation Science and Electrical Engineering,Beihang University,Beijing 100083,China

Virtual Reality Fundamental Research Laboratory,Department of Mathematics and Theories,Peng Cheng Laboratory,Shenzhen 518000,China

AVIC Shenyang Aircraft Design and Research Institute,Shenyang 110035,China

Science and Technology Innovation 2030-Key Project of"New Generation Artificial Intelligence",ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaOpen Fund/Postdoctoral Fund of the Laboratory of Cognition and Decision Intelligence for Complex Systems,Institute of Automa

2018AAA0102403U1913602T21210039194820462103040U20B2071CASIA-KFKT-08

2024

10.1016/j.cja.2023.10.015

中国航空学报(英文版)
中国航空学会

中国航空学报(英文版)

CSTPCDEI
影响因子：0.847
ISSN：1000-9361
年,卷(期)：2024.37(5)