Abstract
In this article, we compare and analyze the YOLO (You Only Look Once) method, widely employed for object detection in digital image processing, with the Haar feature-based cascade classifier method implemented using the OpenCV library. YOLO, a deep learning–based approach, excels in real-time object detection and recognition applications. In contrast, the Haar method utilizes a traditional approach to rapidly identify features. However, significant performance differences exist between the two methods. Experimental results and performance analyses demonstrate that YOLO provides high accuracy rates and real-time processing speeds in object detection tasks. The code implementations presented in this study will be valuable to researchers new to digital image processing. Additionally, YOLO has shown high performance on large and complex datasets by leveraging GPU capabilities. Experiments with various YOLO versions (e.g., YOLOv4, YOLOv5, YOLOv7) have established it as one of the most suitable options for real-time applications, particularly due to its low latency and high accuracy.