基于中心点和双重注意力机制的无人机高分辨率图像小目标检测算法

无人机拍摄的图像具有分辨率高、视野大以及目标小的特点，而现有的目标检测方法对小目标特征的提取能力不足。为此，首先采用以中心点表示目标的检测网络CenterNet，引入可变形双重注意力机制，以提高对小目标的特征表达能力；然后针对原始非极大值抑制难以处理嵌套型冗余框的问题，在冗余检测剔除过程中提出了广义非极大值抑制方法；最后引入LegoNet卷积单元，减少了卷积参数，实现了精度与速度的平衡。实验主要采用的验证数据集为VisDrone2019和UAV_OUC，UAV_OUC数据集相比于VisDrone2019，其图片具有更高的分辨率。相比于CenterNet，所提出的方法在数据集UAV_OUC和VisDrone2019上的检测精度大约分别提高了10%和2%。

Small Target Detection Algorithm of UAV High Resolution Image Based on Center Point and Dual Attention Mechanism

Unmanned aerial vehicle (UAV) images have characteristics of high resolution, large field of vision and small target. However, existing object detection methods are generally insufficient in extracting the features of these small targets. Aiming at this problem, a small target detection algorithm is proposed in this paper. First, in order to improve the ability of feature expression for small targets, CenterNet, a detection network which uses center points to represent small targets, is adopted, and a deformable dual attention mechanism is induced. Then on this basis, for the problem of deficiency of original nonmaximum suppression (NMS) in dealing with nested redundant frames, we propose to use a generalized non-maximum suppression (G-NMS) in the process of redundancy detection elimination. Finally, LegoNet convolution unit is introduced to reduce convolution parameters and achieve balance between precision and velocity. The main validation data sets used in this paper are Visdrone 2019 and UAV_ OUC. Images in UAV_OUC have higher resolution than those in VisDrone2019. Compared with CenterNet, the detection accuracies of UAV_OUC and VisDrone2019 are improved by about 10% and 2% respectively.