Unmanned aerial vehicles (UAVs) have gradually become a major air threat to ships because of small size, good maneuverability, and low cost. Vision-based UAV detection offers one of the main ways to identify and protect against UAVs. Unlike land environment, the weather is complicated at sea. The visibility of an object is undermined by such factors as sea fog and sunlight, which makes it difficult to detect UAVs at sea through visionbased object detection. For the purpose of object detection at sea, this paper proposes a UAV object detection method based on image haze removal. In the proposed method, an improved dark channel haze removal (DCHR) algorithm is utilized to remove haze for and restore video images. Additionally, co-ordinate attention (CoordAttention, CA) is introduced to the lightweight algorithms of You Only Look Once (YOLO) for the object detection in restored video images, so as to improve the precision and speed of detection and reduce the miss rate. Some video images are also taken for detection experiments to verify the feasibility and effectiveness of the proposed method.

Considering the problems of the lack of tracking the intelligent target, which is “smart” enough to escape from the detection by maximizing the estimation error in the current tracking methods by multi-UAVs, a cooperative control method for tracking intelligent target by two UAVs is proposed based on Lyapunov guidance vector field. The mathematical model of an intelligent target is established. According to the multi-UAV distributed intelligent target state fusion estimation method, the target state information is obtained to control two UAVs to circle the target through the Lyapunov guidance vector method. The simulation results demonstrated that the method enables two UAVs to track the intelligent target stably and improve the intelligent target’s positioning accuracy effectively