seat belt detection
تفاصيل العمل
Seatbelt Detection Project
This project aims to implement a seatbelt detection system using image processing techniques in Python with the OpenCV library. The system analyzes an input image to determine whether a seatbelt is present in the driver's seat area. Below is a summary of the process and functionalities:
Project Workflow
Image Preprocessing:
Converts the input image to grayscale for simplicity.
Applies Gaussian blur to reduce noise and smooth the image.
Edge Detection:
Uses the Canny edge detection algorithm to highlight edges in the image, aiding in identifying potential seatbelt lines.
Region of Interest (ROI):
Defines a specific area of the image where a seatbelt is expected, based on the seat position (default is the driver's seat).
The ROI can be adjusted for passenger-side detection as needed.
Line Detection:
Utilizes the Hough Line Transform to detect lines within the ROI that may correspond to the seatbelt.
Line Analysis:
Analyzes the detected lines for features such as slope and length, which align with the characteristics of a seatbelt.
Determines whether a seatbelt is present based on these features.
Postprocessing (Optional):
Allows for drawing bounding boxes or highlights on the image to visually indicate the detection result.
Result Output:
Outputs either "seatbelt detected" or "No seatbelt detected" based on the analysis.
Key Features
Dynamic ROI: Adjusts the region of interest based on seat position.
Edge and Line Detection: Combines robust algorithms (Canny and Hough Transform) to detect seatbelt-like structures.
Customizable Parameters: Thresholds and ROI dimensions can be fine-tuned to improve accuracy for specific scenarios.
Potential Applications
Automotive safety systems for detecting seatbelt usage.
Integration into camera-based monitoring systems for compliance verification.
Enhancing AI models with image-based safety feature detection.
This implementation provides a modular and flexible framework for detecting seatbelts in static images, with the potential for expansion into real-time video feeds.