Revolutionizing Real-Time Object Detection: YOLO and MobileNet SSD Integration
Keywords:You Only Look Once, Real Time Object Detection, MobileNet Single Shot Detector, Convolutional Neural Network
Real-time object detection using machine learning techniques has improved algorithm performance, but issues like blurring, noise, and rotating jitter in real-world images impact detection methods. You Only Look Once (YOLO) is a faster and more accurate real-time object detection algorithm that can detect multiple objects in a single image, unlike other Convolutional Neural Network (CNN) based algorithms. This paper integrates YOLO (version 3) v3 and MobileNet Single Shot Detector (SSD), resulting in faster image detection and accurate localization. It also compares lighter versions of YOLOv3 and YOLOv4 in terms of accuracy.The integration of YOLOv3 and MobileNet SSD enables real-time object detection in various applications like augmented reality, robotics, surveillance systems, and autonomous vehicles. It enhances security, enables immediate responses to potential threats, and allows robots to perceive and interact with their environment. Finally, the work provides an insight into the performance, and capabilities of YOLOv3 and MobileNet SSD, leading to an informed decision-making process for integrating both algorithms in OpenCV.
How to Cite
This is an open Access Article published by Research Center of Computing & Biomedical Informatics (RCBI), Lahore, Pakistan under CCBY 4.0 International License