Neural networks with wavelet transform in the task of detection of overwater objects under low visibility conditions
Abstract
Neural networks with wavelet transform in the task of detection of overwater objects under low visibility conditions
Incoming article date: 19.11.2024This paper considered the problem of detection and classification of surface objects in low visibility conditions such as rain and fog. The focus is on the application of state-of-the-art deep learning algorithms, in particular the YOLO architecture , to improve detection accuracy and speed. The introduction to the problem includes a discussion of the limitations of visibility degradation, the change in shape and size of objects depending on the viewing angle, and the lack of training data. The paper also presents the use of discrete wavelet transform to improve image quality and increase the robustness of the systems to adverse conditions. Experimental results show that the proposed algorithm achieves high accuracy and speed, which makes it suitable for application in drone video monitoring systems.
Keywords: YOLO, wavelet transform, overwater objects, drones, low visibility condition, Fourier transforms, Haar