DEVELOPMENT OF AN IMAGE DETECTION SYSTEM USING MACHINE LEARNING

The rapid advancement in computer vision and machine learning has revolutionized image detection systems, enabling accurate identification and localization of objects within digital images. This project focuses on developing an Image Detection System Using Machine Learning, specifically leveraging the YOLOv5 algorithm, a state-of-the-art convolutional neural network (CNN) known for its speed and accuracy. The system is designed to detect and classify objects in images with high precision, addressing challenges such as varying lighting conditions, occlusions, and background clutter. The project integrates a Django-based web application with a user-friendly interface, allowing users to upload images and receive real-time detection results. Key components include data pre-processing, feature extraction, and model evaluation using metrics like precision, recall, and mean average precision (MAP). The system was trained on the COCO128 dataset, a subset of the Common Objects in Context (COCO) dataset, ensuring robustness across diverse object categories. Evaluation results demonstrate the system's effectiveness, achieving a precision of 68.6% and recall of 63.4%, with further improvements possible through dataset expansion and model fine-tuning. The project highlights the practical applications of image detection in fields such as healthcare, security, and autonomous vehicles, while also acknowledging limitations like dataset size and class imbalance. Future recommendations include real-time processing enhancements and specialization for specific object detection tasks. This work contributes to the growing field of computer vision by providing a scalable, efficient, and accessible solution for image detection, bridging the gap between theoretical research and real-world implementation.