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AI-Vision-Detection: Advanced Object Detection and Visual Analytics

📌 Abstract

AI-Vision-Detection is an efficient object detection system that uses YOLOv8 to analyze images and generate comprehensive visual and statistical insights. This tool is designed for domains like surveillance, traffic monitoring, and retail analytics by offering real-time object detection capabilities and rich analytics.


🚀 Features

  • Object detection using YOLOv8
  • Annotated images with bounding boxes
  • Pie charts of detected object class distributions
  • Histograms of detection confidence scores
  • Boxplots showing bounding box size statistics
  • Robust error handling for smooth execution
  • Summary reports with total and categorized object counts

🛠️ Technologies Used

  • YOLOv8 (Ultralytics) – Pre-trained detection model
  • OpenCV – Image handling and bounding box drawing
  • Matplotlib – Visualization library
  • NumPy – Data processing and math operations
  • Google Colab (Optional) – Easy-to-use notebook interface for running the model

⚙️ Installation

Install the required packages using pip:

pip install ultralytics opencv-python matplotlib numpy

from google.colab import files
uploaded = files.upload()

results = model(image_path)

About

The AI-Vision-Detection project detects objects in uploaded images using a pre-trained YOLOv8 model and provides insightful visual outputs. Users upload images—typically via Google Colab—and the system processes them using OpenCV, Matplotlib, and NumPy to display detected objects with bounding boxes, alongside analytical visualizations like object.

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  • Jupyter Notebook 52.4%
  • TypeScript 33.3%
  • JavaScript 11.4%
  • CSS 2.5%
  • HTML 0.4%