Enhancing Face Recognition Accuracy Using Data Pre-processing Method and YOLO

Abstract

The recognition of objects is an essential aspect of visual perception and finds extensive usage in diverse fields such as self-driving vehicles, security, robotics, and image retrieval. In this study, we investigate the performance of the YOLOv5 (You Only Look Once) algorithm for object detection on the VOC2007 dataset. The YOLOv5 model achieved a moderate overall accuracy and precision, demonstrating its potential for object detection tasks. However, the performance varied across different categories, with lower accuracy observed for less frequent categories and difficulties in distinguishing between closely related categories. We identify potential improvements to the YOLOv5 model's performance, including class balancing using weighted sampling and data augmentation, which may help the model to better learn to detect objects from under-represented categories and improve its ability to distinguish between similar objects. The results of our study imply that the YOLO algorithm has potential for object detection and classification projects in computer vision, however further study and refinement are necessary to broaden its efficacy across a greater variety of object classes and real-world scenarios.