Object Detection using an Advanced Deep Learning Algorithm: YOLOv4

Abstract

This research proposes implementing YOLOv4, a real-time object detection system that can accurately and efficiently recognize objects in photos and videos. The goals include creating the YOLOv4 architecture, generating datasets, training the model, evaluating its performance, deploying it in real-world applications, and providing extensive documentation. Deep learning frameworks such as TensorFlow or PyTorch are used in the implementation, along with advanced techniques such as transfer learning and data augmentation. By curating annotated datasets and refining training techniques, the model hopes to attain high accuracy, precision, and recall in object detection tasks. Performance analysis will compare the model's outcomes to those of cutting-edge systems, assessing its strengths and weaknesses. The deployment phase involves integrating the model into existing systems or creating separate applications for real-world scenarios such as pedestrian and vehicle detection. Comprehensive documentation and a user guide will help developers and users make the best use of the trained model. Overall, this research intends to demonstrate YOLOv4's usefulness and feasibility in developing computer vision technology and supporting the creation of intelligent systems with real-time object identification capabilities.