基于空间相关性增强的无人机检测算法

UAV detection algorithm based on spatial correlation enhancement

针对无人机(UAV)体积小、复杂背景下特征难以提取导致被误检和漏检的问题,提出基于自适应上采样和空间相关性增强的无人机小目标检测方法.采用多尺度的空洞卷积获取重要的上下文信息,然后通过注意力特征融合模块抑制多尺度特征融合造成的信息冲突;采用亚像素卷积和双线性插值自适应融合的新上采样方式,融合更多无人机特征信息,同时平衡计算量;对深层特征图的空间局部特征和全局特征采用空间相关性增强策略,提高复杂背景下前景目标的敏感度,增强目标表达和抑制背景噪声.在自制无人机数据集上进行消融实验和对比实验,与原始YOLOv5算法相比,本算法的m AP0.5和m AP0.5∶0.95分别提高了2.4%和2.7%,检测速度能够达到58.5帧/s;在VisDrone2019数据集上进行验证,本算法较YOLOv5算法的mAP0.5和mAP0.5∶0.95分别提高了4.6%和1.3%.

A small target detection method for unmanned aerial vehicle(UAV)based on adaptive up-sampling and spatial correlation enhancement was proposed,to resolve the problem of false detection and missed detection caused by the small size of UAV and the difficulty of feature extraction under complex backgrounds.Firstly,the important contextual information was obtained by multi-scale dilated convolution,and then the attention feature fusion module was used to suppress the information conflict of multi-scale feature fusion;Secondly,a new up-sampling method of sub-pixel convolution and bilinear interpolation adaptive fusion was adopted to balance the computation and to fuse more UAV feature information;Finally,spatial correlation enhancement strategies for local and global spatial features were performed on deep features to improve the sensitivity of foreground targets in complex backgrounds and enhance target expression to suppress background noise.Ablation experiments and comparative experiments were implemented on the self-made UAV dataset.The mAP0.5 and mAP0.5:0.95 of the proposed algorithm were increased by 2.4%and 2.7%respectively,compared with those of the original YOLOv5 algorithm.Furthermore,the detection speed was able to achieve 58.5 frames per second.The performance of the proposed algorithm was also verified on the VisDrone2019 dataset,and its mAP0.5 and mAP0.5:0.95 were respectively higher than those of the YOLOv5 algorithm by 4.6%and 1.3%.