基于优化CenterNet的低空无人机检测方法

为实现对“低慢小”无人机(UAV)的有效探测, 提升检测精度和定位质量, 提出一种基于联合注意力和CenterNet的低空无人机检测方法。针对通用目标检测算法小目标漏检率高的问题, 引入解耦的非局部算子, 捕捉光学图像目标区域的关联性。利用无人机群个体间的相似性, 将离散的无人机特征相互关联, 降低漏检率。为获得更加精准的检测框, 对CenterNet的标签编码策略和边界框回归方式进行优化, 引入定位质量损失, 提升检测框定位质量。实验结果表明:优化后的S-CenterNet算法相比原始CenterNet算法平均准确率提升了8.9%, 检测框定位质量有明显改善。 

Low-altitude UAV detection method based on optimized CenterNet

To achieve effective detection of "low, slow and small" unmanned aerial vehicle(UAV)and improve detection accuracy and positioning quality, we propose a low-altitude UAV detection method based on joint attention and CenterNet. Aiming at the problem of high miss-detection rate of small targets in general target detection algorithms, a decoupled non-local operator is introduced to capture the relevance of target regions in optical images. Utilizing the similarity between individuals of the UAV group, the discrete UAV features are correlated to each other to reduce the missed detection rate. Moreover, to obtain more accurate detection boxes, we optimized the label coding strategy and bounding box regression method of CenterNet, and the positioning quality loss is introduced to improve the positioning quality of the detection boxes. Experimental results show that the optimized S-CenterNet algorithm has an average accuracy increase of 8.9% compared with the original CenterNet, and the detection boxer positioning quality has been significantly improved.