Design and training of deep CNN-based fast detector in infrared SUAV surveillance system

Abstract

Real-time detection of small unmanned aerial vehicle (SUAV) targets in SUAV surveillance systems has become a challenge due to their high mobility, sudden bursts, and small sizes. In this study, we used infrared sensors and Convolutional Neural Networks (CNN)-based detectors to achieve the real-time detection of SUAV targets. Existing object detectors generally suffer from a computational burden or low detection accuracy on small targets, which limits their practicality and further application in SUAV surveillance systems. To solve these problems, we developed a real-time SUAV target detection algorithm based on deep residual networks. In order to improve the sensitivity to small targets, a laterally connected multi-scale feature fusion approach was proposed to fully combine the context features and semantic features. A densely paved pre-defined box with geometric analysis was used for single-stage prediction. Compared with the state-of-the-art object detectors, the proposed method achieved superior performance with respect to average-precision and frames-per-second. As the training set was limited, to improve generalization, we investigate the benefits introduced by data augmentation and data balance, and proposed a weighted augmentation approach. The proposed approach improved the robustness of the detector and the overall accuracy.