Deep Learning Model for Fish Copiousness Detection to Maintain the Ecological Balance Between Marine Food Resources and Fishermen

Fish copiousness detection is crucial for the monitoring and management of aquatic ecosystems. Deep learning models, such as Mask R-CNN, have shown great potential in accurately detecting and segmenting fish in underwater images. This study explores the effectiveness of Mask R-CNN in fish detection and presents a detailed analysis of its performance. The dataset used for training and testing consists of a large number of underwater images of various fish species. The results show that the Mask R-CNN model can accurately detect and segment fish in complex underwater environments. This study also compares the performance of the Mask R-CNN model with other popular deep learning models and demonstrates the superiority of the Mask R-CNN model in terms of accuracy and efficiency. Overall, the study highlights the potential of deep learning models in fish detection and their usefulness in managing aquatic resources.