Unmanned Aerial Vehicle Classification and Detection Based on Deep Transfer Learning

Currently, major security incidents caused by the “unlicensed flying” of Unmanned Aerial Vehicle (UAV) have emerged one after another, which poses a grave threat to the security issues of public facilities and sensitive areas. Whether it can timely detect and prevent “unlicensed flying” of UAV has become a social concern. In response to this demand, the transfer learning method is adopted in this paper to conduct twoclassification and detection on UAV images. Image recognition technology based on transfer learning is an effective method to improve recognition accuracy by applying deep learning models to small samples. Different from the large number of training samples required by deep learning, transfer learning transfers the weights of the pre-trained deep neural network, and uses only small sample data to obtain good results in UAV image recognition. First of all, this paper proposes to construct a UAV data set according to different types of UAV shape structures, to perfect the classification and detection effect and the generalization ability of the model. Then, based on the transfer learning method, experimental comparison is made between three classic deep convolutional neural network classification models (Inception V3, ResNet 101 and VGG16) and two classic deep convolutional neural network detection models (Faster RCNN and SSD). Finally, an experimental evaluation is conducted on the collected UAV test data set. Compared with the traditional recognition model, the image classification model based on transfer learning employed in this paper has achieved important improvements in accuracy, recall and precision. Especially in the InceptionV3 model of transfer training, the recall reaches 96.98%. In addition, the image detection model based on transfer learning has achieved good detection results in accuracy, recall and F1-score.