Enhanced Object Detection Based on YOLOv9 for Marine Life

Object detection for marine life leverages advanced computer vision techniques to identify and monitor various marine species and their behaviors in underwater environments. This technology enables efficient data collection, which is crucial for conservation efforts and the study of marine ecosystems. By automating the analysis of underwater imagery and video, researchers can better understand marine biodiversity and respond to environmental changes. However, existing methods for marine life detection often face challenges such as low visibility, varying lighting conditions, and occlusions caused by underwater debris or other marine creatures. Furthermore, these approaches' accuracy and scalability are limited by the time-consuming and error-prone nature of manual dataset processing. The lack of robust datasets and domain-specific models further hampers the effectiveness of traditional methods. Our project seeks to overcome these constraints by creating an enhanced object detection based on YOLO v9 that is more resilient and environment-specific. By incorporating advanced deep learning algorithms and enhancing training with diverse, highquality datasets, our approach improves accuracy and resilience against common underwater challenges. This advancement will streamline the identification process, enabling more precise and scalable marine life monitoring.