Multi-scale Feature Fusion Based Drone Detection

• 1、School of information and communication engineering, University of Electronic Science and Technology of China, Chengdu

Abstract：With the growing concern regarding the safety issues of drones, the importance of drone detection is emphasized in many places such as airports, military bases, prisons, and so on. Visual based drone detection can promote the development of drone countermeasures technology to a certain extent. One of the challenges in visual-based drone detection is that imaging scale changes greatly, the background is complex, and the interference target usually appears. Convolutional Neural Network can learn the target features with strong representation ability, but the low-level feature semantic information and high-level feature detail information of the network are insufficient, which makes it unable to detect large and medium-sized targets with good robustness. Based on this motivation, this paper constructed a drone detection dataset, uses Res2net to extract multi-sensory field features, proposed a new hybrid feature pyramid structure improving network performance from fine-grained multi-scale feature extraction and hierarchical multi-scale feature fusion, and designed a multi-scale feature fusion algorithm for drone target detection. Based on the drone dataset constructed in this paper, the discrimination of drones, birds, and airplanes is over 95% in our experiments

Keywords： drone detection image processing computer vision multi-scale feature fusion