Introduction to Object Detection Concepts

Aug 26, 2024

Object Detection 101 Course Overview

Course Introduction

  • Objective: Introduction to object detection with theory and practical projects.
  • Four real-world projects included:
    1. Car Counter
    2. People Counter
    3. Personal Protective Equipment Detector
    4. Poker Hand Detector
  • Course structure:
    • Theoretical background
    • Practical implementations
    • Installation and setup of YOLO v8

Object Detection Basics

Definition

  • Computer vision technique for locating objects in images/videos.
  • Outputs: bounding box information + classification of objects.

Key Concepts

  • Object Classification: Identifying the type of object in an image (single class output).
  • Object Detection: Locating and identifying multiple objects in an image (multiple bounding boxes + classes).
  • Object Segmentation: Identifying and separating objects at the pixel level (exact shape extraction).

Historical Overview

  • 1970s: Automated methods developed.
  • 2001: Viola-Jones algorithm for face detection introduced.
  • 2005: Histogram of Oriented Gradients (HOG) developed for shape detection.
  • 2012: AlexNet won ImageNet Challenge using CNN (but focused on classification).
  • 2014: Faster R-CNN model developed.
  • 2015: YOLO (You Only Look Once) introduced, providing real-time detection.

YOLO Versions Evolution

  • YOLO continues to evolve with annual updates.
  • New versions often improve efficiency and detection accuracy.

Evaluation Metrics

Important Metrics

  • Intersection over Union (IoU): Measures localization accuracy of predicted bounding boxes. Values range from 0 (no overlap) to 1 (perfect overlap).
  • Mean Average Precision (mAP): Combines precision and recall across multiple classes.

Environment Setup

Python Installation

  • Recommended version: Python 3.10 (avoid the latest for stability).
  • Install PyCharm for development.

YOLO Installation

  • Install YOLO using pip: pip install ultralytics.
  • Ensure NVIDIA drivers and CUDA toolkit are installed for GPU support.

Project Implementation

Car Counter and People Counter Projects

  • Use YOLO v8 for implementing object detection in real-time.
  • Built-in mechanisms for object tracking and counting (e.g., cars and people).

Training Custom YOLO Model

  • Use personal dataset for training custom models (e.g., PPE detection, poker hand detection).
  • Utilize Google Colab for online training or local setups with specified configuration.

Poker Hand Detection

  • Classify poker hands based on various combinations (e.g., pairs, flushes, etc.).
  • Implement a function to evaluate hands and determine the best possible outcome.

Final Notes

  • Emphasis on practical skills and real-world applications.
  • Iterative learning approach with hands-on project experience.

Additional Resources

  • Access to datasets for training.
  • Instructions for setting up a development environment.