An Uncertainty Quantization-Based Method for Anti-UAV Detection in Infrared Images  

作　　者：Can Wu Wenyi Tang Yunbo Rao Yinjie Chen Hui Ding Shuzhen Zhu Yuanyuan Wang 

机构地区：[1]School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu,610054,China [2]State Key Laboratory of Air Traffic Management System,Nanjing,210000,China [3]National Airspace Management Center,Beijing,100094,China

出　　处：《Computers, Materials & Continua》2025年第4期1439-1458,共20页计算机、材料和连续体(英文)

基　　金：supported by the Science and Technology Project of Sichuan(Grant No.2024ZHCG0170);the National Key Research and Development Program of China,“Key Technologies for Instrumentation and Control System Program Security Based on Blockchain”(Project No.2024YFB3311000);States Key Laboratory of Air Traffic Management System(Grant No.SKLATM202202);the Chengdu Science and Technology Project(Grant No.2022-YF05-00068-SN).

摘　　要：Infrared unmanned aerial vehicle(UAV)target detection presents significant challenges due to the inter-play between small targets and complex backgrounds.Traditional methods,while effective in controlled environments,often fail in scenarios involving long-range targets,high noise levels,or intricate backgrounds,highlighting the need for more robust approaches.To address these challenges,we propose a novel three-stage UAV segmentation framework that leverages uncertainty quantification to enhance target saliency.This framework incorporates a Bayesian convolutional neural network capable of generating both segmentation maps and probabilistic uncertainty maps.By utilizing uncer-tainty predictions,our method refines segmentation outcomes,achieving superior detection accuracy.Notably,this marks the first application of uncertainty modeling within the context of infrared UAV target detection.Experimental evaluations on three publicly available infrared UAV datasets demonstrate the effectiveness of the proposed framework.The results reveal significant improvements in both detection precision and robustness when compared to state-of-the-art deep learning models.Our approach also extends the capabilities of encoder-decoder convolutional neural networks by introducing uncertainty modeling,enabling the network to better handle the challenges posed by small targets and complex environmental conditions.By bridging the gap between theoretical uncertainty modeling and practical detection tasks,our work offers a new perspective on enhancing model interpretability and performance.The codes of this work are available openly at https://github.com/general-learner/UQ_Anti_UAV(acceessed on 11 November 2024).

关 键 词：Object segmentation uncertainty quantification bayesian convolutional neural network 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]