Deep Drowsiness Detection Using YOLO, Pytorch and Python

Overview

Learn how to implement a deep drowsiness detection system using YOLO, PyTorch, and Python in this comprehensive tutorial video. Discover the process of leveraging YOLO object detection for driver safety by creating a fine-tuned, custom object detection model. Follow along to install Ultralytics YOLOv5, detect objects from images and pre-recorded videos, perform real-time object detection using OpenCV, fine-tune a drowsiness model with YOLOv5 and PyTorch, and implement real-time drowsiness detection. Gain hands-on experience with step-by-step instructions, from setting up dependencies to training a custom YOLO model and applying it to detect driver drowsiness. Access provided resources, including GitHub code repository, PyTorch installation guide, and additional tools like LabelImg for efficient implementation.

Syllabus

- Start
- Introduction
- Gameplan
- How it Works
- Tutorial Start
- 1. Install and Import Dependencies
- 2. Load Model
- 3. Make Detections using Images
- 4. Real Time Detections and Object Detection using Videos
- 5. Train a Custom YOLO Model
- 6. Detecting Drowsiness
- Ending