A target detection algorithm of illegal satellite TV receiver based on the improved YOLO v9

In the process of detecting illegal Satellite TV receiver by the unmanned aerial vehicle aerial photography, in order to solve the problems of small size for illegal Satellite TV receiver, low correctness and efficiency of manual identification during the detection process. In this paper, we propose a method of illegal Satellite TV receiver target detection and visualization based on improved YOLO v9. In order to improve the fitting ability of the model, this paper processed the dataset with data enhancement techniques to extend the data. Due to the height of the aerial photography and the angle of the Satellite TV receiver placement, the shape and size of the Satellite TV receiver in the image or video are not fixed, resulting in the accumulation of recognition errors in deep learning networks. In order to solve this problem, this paper incorporates the Alterable Kernel Convolution (AKConv) ideas to improve the algorithm in YOLO v9. After experimental comparison and analysis, the algorithm used in this paper reaches 92.7% in accuracy; mAP_0.5 reaches 97.3%, which are better than other algorithmic networks, and satisfy the accuracy and real-time requirements of illegal Satellite TV receiver detection.