Development of a deep learning-based system in Python 3.9 with YOLOv5: A case study on real-time fish counting based on classification
This study developed a real-time fish classification and counting system for six types of fish using the YOLOv5 machine learning model with high accuracy. The system achieved an F1-score of 0.87 and a precision confidence curve with an all-classes value of 1.00 at a confidence level of 0.920, demonstrating the model's reliability in object detection and classification. Real-time testing showed that the system could operate quickly and accurately under various environmental conditions with an average inference speed of 30 FPS. However, several challenges remain, such