Combining Faster-RCNN and Convolutional Siamese Network for Aerial Vehicle Tracking

Drones bring convenience to humans as well as threats. According to the characteristics of drones, it is imperative to develop corresponding defense systems to detect, track, and strike intrusive drones. Based on the twin network research in the field of target tracking, this paper proposes a detection and tracking fusion algorithm, using new amplification methods to solve the problem of poor detection accuracy of small targets, and using progressive labeling strategies to solve the problem of difficulty in acquiring labeled data sets. The engineering method alleviates the problem of poor generalization ability of the algorithm under some extreme conditions, Comparing the performance of the proposed algorithm with the typical target detection algorithm on the UAV data set, the efficiency of the algorithm is verified.