This project implements a real-time object detection system using YOLOv8, OpenCV, and Python. It captures live webcam input and detects multiple object classes with high accuracy and low latency. The system is ideal for applications like surveillance, automation, and interactive AI.
Object detection enables machines to recognize and localize objects within images or videos. YOLOv8 (You Only Look Once, version 8) offers fast and accurate single-shot detection. Combined with OpenCV, we create a real-time detection pipeline suitable for practical deployment.
ultralytics package.OpenCV to capture webcam input frame-by-frame.
The model was tested on:
Indoor scenes: detecting laptops, phones, people
Outdoor scenes: vehicles, signboards, pedestrians
Hardware: Standard laptop with CPU
Model: YOLOv8-Nano
YOLOv8's lightweight architecture makes it ideal for real-time applications. This project proves its feasibility in live object detection without GPU support. Future plans include:
Streamlit or Flask deployment
Object tracking integration
Edge-device optimization
š GitHub Repo: Link text
š YOLOv8 Docs: https://docs.ultralytics.com