Cognitive Analysis of Underwater Object Detection Using Deep Learning for Marine Exploration

Object detection is an important aspect of numerous real-world applications such as autonomous systems, medical imaging, and surveillance. As deep learning continues to advance, real-time object detection has been greatly enhanced in terms of accuracy and computational efficiency. In this study, a sophisticated object detection model, YOLOv11, was developed to improve detection accuracy, speed, and robustness in different environments. YOLOv11 uses optimized convolutional neural network (CNN) structures, enhanced feature extraction methods, and advanced anchor-free mechanisms to provide high accuracy with low latency. The model is tested on benchmark datasets and shows better performance in terms of mean average precision (mAP) and intersection over union (IoU) than current state-of-the-art techniques. In addition, this research offers a thorough investigation of the YOLOv11 model, outlining network architecture enhancements, loss function optimization, and real-time inference. The outcomes of this study enhance object detection methods, facilitating more accurate and efficient deployment in real-world applications in seascapes.