Accurate detection of underwater objects using EfficientNet Algorithm

The primary objective of this research is to increase accuracy in underwater object identification by developing and implementing a cutting-edge model that makes use of the EfficientNet approach. Methods and Materials: The existing work consisted of three groups. Group 1 utilized the YOLOv4 algorithm to improve speed and accuracy. Group 2 employed the SemiDsConv algorithm for image capture, while Group 3 utilized the FIou function to identify difficulties in scenarios. The proposed work introduces Group 4, which utilizes the EfficientNet algorithm for underwater object detection, achieving astounding accuracy.Results: With an accuracy of 98.06%, the EfficientNet underwater object model performed better than YOLOv4. This demonstrates how useful it is for underwater object identification and highlights the need for precise surveillance. Conclusion: Outperforming YOLOv4 in underwater object identification, the EfficientNet algorithm shows promise for improving underwater surveillance devices.