Design and Implementation of a Real-Time Object Counting Detector: A Comprehensive Non-linear Approach

Abstract

This study presents a thorough, nonlinear method for creating and putting into practice a real-time item counting detector. The process includes choosing suitable deep learning frameworks, building object classes, gathering various datasets, preparing data, and using pre-trained models, such as YOLO and SSD. Real-time processing is achieved by means of Non-Linear optimization strategies and transfer learning for fine-tuning. The study explores post-processing techniques, hardware issues, and the complexities of working with camera feeds in order to achieve accurate counting. The study highlights the mathematical models related to deep learning methods, including FPN, RetinaNet, LSTM, and the suggested DeepSORT. It does this by offering an informative comparison table that shows recall, accuracy, precision, and F1 Score. Finally, the study sheds insight on the dynamic history of the discipline while acknowledging the fundamental contributions of the early real-time object identification attempts. This paper provides a thorough grasp of the complexities involved in the process, making it an invaluable resource for anybody looking to create and implement efficient real-time item counting detectors.