Deep WaveNet-based YOLO V5 for Underwater Object Detection

Detecting objects under the water is always a difficult task. Due to the unique characteristics of underwater settings, identifying underwater objects can be difficult. When light penetrates deep within the water, the aqueous medium exhibits diffraction and scattering. This leads to murky, obscured films and photos, which make interpretation more difficult. Underwater object detection has become one of the most important objectives in deep-sea exploration. To overcome the above issues, we propose a methodology based on the YOLO model in which the images will be passed through for detection. The images are trained using the CNN-based deep wave net algorithm which enhances the faded or distorted image further making the YOLO algorithm infer better results. The YOLO models are compared with other detection models and different activation layers are also compared.