Real-Time Object Detection Using Yolov9c and Flask Web Application

This research utilizes the YOLOv9c model, an advanced object detection web application, integrating it into a Flask framework. The proposed method involves dataset gathering from users, data pre-processing, model construction, and evaluation using metrics such as accuracy, precision, and IoU. The results demonstrate substantial improvements compared to earlier YOLO versions, emphasizing the model's robustness and efficiency. The application supports static images, video uploads, and live webcam streams, demonstrating high accuracy across modalities: 94.7% for image detection, 93.5% for video detection, and 92.8% for webcam stream detection.