Deep Learning Based UAV Target Recognition and Countermeasures
With the wide application of low-altitude UAVs in military and civil fields,their safety hazards need urgent attention.In this paper,a detection method based on the improved YOLOv7 model is proposed,and an attention mechanism is introduced to strengthen the model's ability to express the characteristics of the target area.An improved StrongSORT tracking algorithm is also proposed to optimize the tracking performance.These research results improve the accuracy and real-time performance of detection and tracking,expand the surveillance field of view through the gimbal active tracking control algorithm,and enhance the tracking flexibility of the system.A complete infrared UAV detection and tracking system is finally realized,which meets the real-time tracking requirements and explores its potential applications in anti-UAV systems in the civil sector.
deep learningUAV target recognitionimproved YOLOv7attention mechanismStrongSORT tracking algorithmgimbal active tracking control algorithm
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