Machine Learning Based Theft Detection Using Yolo Object Detection

Abstract

Abstract: The escalating need for advanced security measures has spurred innovation in the field of computer vision and machine learning. This research project is dedicated to the development of a cutting-edge theft detection system, leveraging machine learning techniques and object detection algorithms. With a focus on enhancing security in surveillance systems, the project addresses the critical challenge of real-time theft detection in various environments. The core of this project involves the exploration and implementation of state-of-the-art machine learning models, including the YOLO (You Only Look Once) algorithm, to detect theft and suspicious activities. An extensive literature survey is conducted to reviewed the research methodology encompasses data collection, preprocessing, model selection, and rigorous evaluation. Diverse datasets are curated, annotated, and used to train and fine-tune the machine learning models. Model performance is assessed using a range of evaluation metrics, including precision, recall, F1 score, and real-time processing capabilities. The project also addresses practical challenges, such as privacy concerns and adaptation to varying environmental conditions. Privacy-preserving techniques, ethical considerations, and adaptability to diverse theft scenarios are integral components of the research. Existing methodologies, benchmark datasets and privacy considerations in the context of theft detection.