Image Detection Using YOLOv5

This project successfully implemented an image detection model with YOLOv5 to identify 'coir,' 'rubber,' and 'rubber coir moulded mats.' The project involved dataset collection by capturing diverse mat varieties across Alappuzha, annotation and conversion using Python, lxml, and PyQt5 for image processing, labelImg for annotation in PascalVOC format, and a custom script for YOLO format conversion. The dataset was organized into training and validation sets for optimal model performance. Model training and detection were performed using YOLOv5 with optimal parameters to enable efficient object detection. Additionally, a user-friendly GUI was developed using Tkinter to enhance the overall user experience. This project showcases expertise in computer vision, deep learning, and effective project management, leading to precise detection in industrial applications.