Real Time Object Detection Using YOLOv4

Abstract

Abstract: This research paper introduces a robust real-time object detection system, leveraging the cutting-edge capabilities of the YOLOv4 (You Only Look Once) deep learning framework.The integration of this system with a webcam in the Google Colab environment forms a novel and accessible platform for live object detection. The paper meticulously details the methodology behind theintegration, providing comprehensive insightsinto the intricatesteps taken to achieve seamless real-time detection. Key aspects covered include the configuration and acquisition of YOLOv4 model weights, setup of the Google Colab environment, and the challenges encountered in optimizing and streaming webcam feeds in this unique computational setting. Implementation details are articulated through Python code snippets, offering a practical guide for researchers and practitioners interested in replicating or extending the system. The code encompasses the initialization of YOLOv4, handling real-time webcam feeds, and harnessing GPU acceleration within the Google Colab framework to ensure optimalperformance. Results presented in the paper showcase the efficacy of the integrated system through real-time object detection on webcam feeds. Evaluation metrics, including accuracy and processing speed, provide a quantitative assessment of the system's performance. Comparative analyses against other state-of-the-art object detection methodologies further highlight the strengths and capabilities of the YOLOv4-based system. The discussion section delves into the advantages and limitations of the implemented system, offering insights into potential applications across domains such as surveillance, robotics, and human-computer interaction. The user-friendly nature of the Google Colab environment is emphasized, promoting accessibility to advanced computer visiontechnologies. In conclusion, this research significantly contributes to the democratization of computer vision technologies, providing a comprehensive blueprint for researchers and practitioners to implement real-time object detection systems. The paper concludes with recommendations for future research directions and improvements to further enhance the system's versatility and performance.