光学精密工程2024，Vol.32Issue(24)：3616-3631,16.DOI:10.37188/OPE.20243224.3616
跨层注意力交互下的多特征交叉无人机图像检测
Multi-feature cross UAV image detection algorithm under cross-layer attentional interaction
摘要
Abstract
Aiming at the problems of complex background of aerial images,dense targets,and uneven tar-get scale distribution in UAV traffic inspection,a multi-feature crossover under cross-layer attentional in-teraction(Multi-feature crossover under cross-layer attentional interaction,MCAI)UAV target detection algorithm was proposed.Firstly,an Adaptive Cross-layer Attentional Interaction(Adaptive Cross-layer Attentional Interaction,ACAI)module was designed in the backbone network part so that the model fo-cused on the key feature regions to achieve effective screening of global key feature information,thus fad-ing the influence of the complex background.Secondly,a deformable self-attentive encoder(Deformable Encoder,DeEncoder)was designed,which compensated for the lost target features by expanding the fea-ture layer receptive field.Finally,in order to effectively identify tiny targets at different scales in the re-gion,the multi-scale cross-fusion module(Multi-scale cross fusion module,MSCF)was proposed,which fused shallow spatial information and deep semantic information by combining the wavelet transform and feature representation in order to efficiently capture the fine-grained features of targets at different scales.The experimental results on the VisDrone 2019-DET,BDD-100K dataset,and LZTraffic Video dataset show that MCAI improves mAP0.5 by 3%,2.2%,and 4.5%,respectively,compared to the RT-DE-TR model,which significantly improves the detection accuracy of the UAV inspection.In addition,in the cloudy and rainy scenario,the mAP0.5 of MCAI improves by 2.1%compared to the RT-DETR model,with better extreme weather robustness performance.关键词
无人机巡检/目标检测/注意力交互/编码器/小波变换Key words
UAV inspection/target detection/attentional interaction/encoder/wavelet transform分类
信息技术与安全科学引用本文复制引用
张志豪,杜丽霞,侯越,郝紫微,尹杰..跨层注意力交互下的多特征交叉无人机图像检测[J].光学精密工程,2024,32(24):3616-3631,16.基金项目
国家自然科学基金(No.62063014,No.62363020) （No.62063014,No.62363020）
甘肃省自然科学基金(No.22JR5RA365) （No.22JR5RA365）
