Real-Time Object Detection Using Deep Learning

Abstract

Abstract: Combining deep learning with computer vision has enabled significant advancements in real-time object detection. Leveraging the capabilities of the YOLOv8 architecture, the proposed system efficiently detects and tracks objects across various input sources, including static images, live webcam feeds, and video streams. By integrating a Flask-based web application with the YOLOv8 model, the system delivers a secure, responsive, and user-friendly interface for real-time detection. The model is trained on a custom dataset, enhanced through extensive data augmentation techniques to improve generalization across diverse environments. Key performance metrics such as Mean Average Precision (mAP), precision, recall, and frames per second (FPS) were used to evaluate accuracy and speed. Results demonstrated robust performance with up to 75% mAP at IoU 0.5 and realtime processing speeds of 25–30 FPS on GPU-enabled systems. Challenges such as performance degradation under low-light and high-occlusion conditions were addressed through thoughtful dataset preparation and architectural tuning. The system is scalable and adaptable, making it suitable for real-world applications such as surveillance, industrial monitoring, and accessibility tools.