Tiny drone object detection in videos guided by the bio-inspired magnocellular computation model

Gang Wang, Xin Yang, Liang Li, Kai Gao, Jin Gao, Jia-yi Zhang, Da-jun Xing, Yizheng Wang

Abstract: Highlights•Drone detection is desired but current methods underperform against complex backgrounds.•A bio-inspired drone detection method utilizing static and motion information is designed.•A retinal magnocellular computation model is used to extract the motion strength.•The motion strength is employed to guide the deep networks for tiny object detection.•The proposed method significantly outperforms the competing state-of-the-art methods.