Application of Convolutional Neural Networks with Wavelet Transform to Maritime Border Patrol Tasks

This paper explores the application of wavelettransformed convolutional neural networks for maritime border patrol. Unmanned aerial vehicles (UAVs) are widely used in security and monitoring tasks, but they have limitations in the quality of cameras, which affects the resolution and clarity of the resulting images. In this regard, it is proposed to use wavelet-transformed convolutional neural networks to improve object detection. This approach can improve the efficiency and accuracy of maritime border patrol, providing more reliable and prompt detection of suspicious objects and events