Yolo- Driven Machine Learning System for Enhanced Object Recognition and Automation

This paper presents an object recognition system that uses machine learning to improve automation in industries. Using deep learning models, the system achieves high accuracy in real-time object identification, improving efficiency in manufacturing, logistics, and robotics. The aim of this project is to create an object detection system that is practical, easy to use, and deployable so that it can be included into real-world applications. The real-time operation of the system guarantees prompt item recognition and categorization in a variety of environmental circumstances. As a result, it is a useful instrument for assistive technologies, security systems, driverless cars, and industrial automation. The creation and training of a YOLO-based model, optimized with a variety of datasets to optimize identification accuracy, is a crucial part of this research. The final deliverables include a fully functional object identification application, a well-trained deep-learnings model, and detailed documentation documenting the technique, implementation process, and experimental outcomes. The efficiency, scalability, and resilience of the system are shown by extensive testing and validation. The results demonstrate its efficacy in real-world implementation by confirming its capacity to function reliably under a variety of circumstances. A fully functioning object detection application, a trained YOLO model, and thorough documentation detailing the technique and results are all included in the final product. Experimental results validate its robustness and scalability. These groundwork for future developments is laid by this research, which adds to the expanding field of AI-driven object identification.