航空学报2025，Vol.46Issue(23)：104-118,15.DOI:10.7527/S1000-6893.2025.31968
动态亮度重建的无人机可见光-红外融合目标检测
Dynamic brightness reconstruction for UAV visible-infrared fusion object detection
摘要
Abstract
The visible-infrared fusion object detection of Unmanned Aerial Vehicles(UAV)has important application value in military and civilian fields such as disaster rescue,security monitoring,and battlefield reconnaissance.How-ever,under low illumination conditions,existing fusion strategies have several limitations,including ignoring uneven lighting in different areas of the same scene and over-reliance on infrared modalities,which results in the potential rich semantic information of visible images in low illumination conditions.In addition,low light further exacerbates the diffi-culty of cross modal fusion.To address the above problems,a dynamic brightness reconstruction for UAV visible-infrared fusion object detection method is proposed.Firstly,a Super pixel Dynamic Illumination aware Mask(SDIM)module was designed using prior local illumination information.By simulating the dependence of real scenes on differ-ent modalities and introducing superpixel information,the problem of object edge feature loss in existing methods was solved.Secondly,considering the problem of feature degradation in low light visible images,a Low Illumination Image Enhancement(LIIE)module was designed to achieve end-to-end optimization of visible image key semantic adaptive enhancement for detection tasks.Finally,a Multi-Scale Feature Cross-Attention Fusion(MFCF)module was de-signed to address the fusion conflicts caused by cross modal feature heterogeneity.The module constructs a bimodal feature interaction space through a hierarchical cross attention mechanism and adaptively fuses multi-scale features using a dynamic weight allocation strategy.Based on the typical visible-infrared fusion object detection datasets Drone-Vehicle and VEDAI,the experimental results verified the effectiveness and robustness of the proposed method in visible-infrared fusion target detection tasks under low illumination conditions.Specifically,compared with existing ad-vanced fusion detection algorithms,the proposed method improved the average accuracy(mAP)by 2.3%and 2.2%respectively while maintaining a low number of parameters,and compared with the widely used single-mode YOLOv8 algorithm,the mAP has increased by up to 12.9%.In addition,cross scene experimental results based on the LIS real low illumination dataset further validated the excellent generalization capability of the proposed method.关键词
低照度/无人机/可见光-红外/深度学习/目标检测Key words
low illumination/UAV/visible-infrared/deep learning/object detection分类
航空航天引用本文复制引用
LIU Kui,SUN Hao,WU Han,JI Kefeng,KUANG Gangyao..动态亮度重建的无人机可见光-红外融合目标检测[J].航空学报,2025,46(23):104-118,15.基金项目
国家自然科学基金(61971426) National Natural Science Foundation of China(61971426) （61971426）
