Research on anti-UAV detection algorithm based on convolutional neural network

In this paper, a novel Convolutional Accelerated Gradient (CAG) algorithm is proposed, which combines convolutional neural network (CNN) with Nesterov Accelerated Gradient (NAG) optimization algorithm and is applied to the field of anti-UAV detection. By introducing NAG's "forward looking" strategy, CAG algorithm can predict the gradient direction in advance, accelerate the training process of CNN model, and improve the convergence speed. In anti-UAV detection, CAG algorithm can more effectively extract UAV features from image and video data to achieve accurate detection and recognition. Finally, through the comparison experiment and analysis between the framework of this paper and the traditional detection algorithm, the experimental results show that CAG algorithm performs well in the UAV target detection task, significantly improves the detection accuracy and detection error, and provides a new idea and method for the development of anti-UAV technology. This proves the feasibility and effectiveness of this architecture in the anti-UAV detection based on convolutional neural network.