An Improved YOLOv5 for Real-time Mini-UAV Detection in No Fly Zones

In the past few years, the manufacturing technology of mini-UAVs has undergone major developments. Therefore, the early warning optical drone detection, as an important part of intelligent surveillance, is becoming a global research hotspot. In this article, the authors provide a prospective study to prevent any potential hazards that mini-UAVs may cause, especially those that can carry payloads. Subsequently, we regarded the problem of detecting and locating mini-UAVs in different environments as the problem of detecting tiny and very small objects from an air image. However, the accuracy and speed of existing detection algorithms do not meet the requirements of real-time detection. For solving this problem, we developed a mini-UAV detection model based on YOLOv5. The main contributions of this research are as follows: (1) a mini-UAV dataset of air pictures was prepared using Dahua multi-sensor camera: (2) a tiny and very small object detection layers are added to improve the model's ability to detect mini-UAVs. The experimental results show that the overall performance of the improved YOLOv5 is better than the original. Therefore, the proposed mini-UAV detection technology can be deployed in monitor center in order to protect a No Fly Zone or a restricted area.