无人机行业在给社会各领域带来便利的同时,也对社会和军事安全构成了严重威胁。因此,快速准确地定位识别未知的无人机显得十分重要。对此,文中通过搭建深度残差网络和预测网络,提出了一种基于红外图像的低空无人机检测识别方法。首先,通过残差网络对红外图像的深度特征进行提取,然后预测网络采用多尺度模型结构对提取的特征进行位置和类别的预测,最后经过非极大值抑制的方式对重复的结果进行剔除。与其他方法的对比实验结果表明,mAP值达到了78.21%,检测速度约为28张/s,检测识别性能优于其他方法。

The drone industry not only brings convenience to the society, but also poses a serious threat to the social and military security. Therefore, it is very important to quickly and accurately locate and identify unknown UAVs. In this paper, a detection and recognition method of low-altitude UAV based on infrared image was proposed by building a deep residual network and prediction network. First, the depth features of infrared images were extracted by residual network, and then the prediction network uses multi-scale model structure to predict the location and category of the extracted features. Finally, the repeated results are eliminated by means of non-maximum suppression. Compared with other methods, the experimental results show that the mAP value reaches 78.21%, the detection speed is about 28 images/second, and the detection and recognition performance is better than other methods.