Comparison of You Only Look Once (YOLO) and Single Shot Multibox Detector (SSD) Methods for Object Detection Using OpenCV

Abstract

Object detection is a crucial field in image processing and computer vision, playing a vital role in applications such as security, industrial automation, human-computer interaction, and autonomous vehicles. As the demand grows for fast, accurate, and real-time systems, deep learning-based object detection methods have become a primary choice. This study aims to evaluate and compare the performance of two popular methods, You Only Look Once (YOLO) and Single Shot MultiBox Detector (SSD), using OpenCV as the development platform. The evaluation focuses on key parameters, including detection accuracy, processing speed, resource efficiency, and real-time applicability. The COCO dataset was used for testing, as it offers diverse object categories with variations in lighting conditions, background, and object sizes. Pre-trained YOLOv3 and SSD models were implemented using Python. The results indicate that YOLO achieves higher processing speed, with an average frame rate of up to 45 fps, making it ideal for fast-response applications such as surveillance systems. Conversely, SSD demonstrates better accuracy in detecting small objects, although its processing speed is slightly lower than YOLO. Further analysis reveals that YOLO relies heavily on GPU for optimizing processing speed, whereas SSD depends more on CPU, making it suitable for devices with limited resources. This study provides valuable insights for developers and researchers in selecting the most appropriate object detection method for specific needs, whether for real-time applications or scenarios requiring high accuracy. Understanding the strengths and limitations of each method enables optimized object detection technology for various future applications.