This paper proposes an anti drone intelligent tracking and aiming system based on computer vision and deep learning optimization to address the challenges of dataset scarcity, difficulty in small target recognition, low efficiency of embedded deployment, and limited detection range in drone visual inspection. A ground to air unmanned aerial vehicle (UAV) dataset GA Fly, consisting of 10800 4K images, was constructed in this study, covering diverse shooting angles, lighting conditions, and target scales. The experiment showed that low altitude complex backgrounds significantly interfere with detection accuracy. At the algorithmic level, a YOLO Drone detection model was designed, which achieved a 6.1% improvement in mAP50:95 and an 82% reduction in the number of detection head parameters in small object detection tasks through multi-scale dense connection modules (MDC) and mixed head structures (MSH); By combining LAMP pruning and knowledge distillation techniques, the model parameters were compressed by 88% while maintaining an accuracy improvement of 0.4%. In terms of embedded deployment, based on the Jetson AGX Orin platform, real-time inference of 14.7ms is achieved through CUDA and TensorRT acceleration, which is 54.4% faster than the unoptimized model. In addition, a pan tilt camera tracking system has been developed, which uses PD control algorithm and 12 bit PWM drive to stably track flight targets at a lateral speed of 53.6km/h and a longitudinal speed of 18km/h within a range of 7-66 meters. Experimental verification shows that the detection accuracy of our method in complex backgrounds is improved by 30.6% compared to the traditional YOLO series, with a gimbal tracking response speed of 31Hz and an embedded inference delay of less than 15ms. Future research will focus on multimodal fusion detection, lightweight neural architecture search, and hardware system upgrades.

Wenjie Jiang, Liuqing Yang. Anti drone intelligent tracking and aiming system optimized by computer vision and deep learning. Machine Learning Theory and Practice (2025), Vol. 5, Issue 1: 53-63. https://doi.org/10.38007/ML.2025.050106.

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