Object Detection Comparison Between YOLO and OpenCV

Abstract

In the 21st century, automation significantly shapes global development, particularly in image recognition technology, essential in robotics and computer vision. This thesis compares two leading object detection frameworks: YOLO (You Only Look Once) and OpenCV. YOLO utilizes a neural network approach for real-time detection by predicting bounding boxes and class probabilities simultaneously, achieving substantial accuracy at high processing speeds. OpenCV offers diverse functionalities, including traditional methods like Haar Cascades and HOG+SVM, alongside integration capabilities for deep learning models. The YOLO model employed in this study was trained on a dataset of images and evaluated with 10,000 images, revealing significantly higher accuracy compared to traditional methods like OpenCV’s OCR techniques. The effectiveness of YOLO in defining regions of interest notably enhances OCR accuracy, demonstrating YOLO’s superior practical applicability in object localization tasks.