The document outlines an algorithm for traffic sign detection in images captured by a low-resolution camera on moving vehicles, focusing on color and shape detection for yellow warning and red stop signs. It describes the steps of color enhancement, color segmentation, edge detection, and Hough transform, along with experimental results that highlight the algorithm's accuracy and limitations, such as its sensitivity to background complexity and illumination conditions. Future work suggests employing machine-learning techniques to improve detection accuracy.

1. Traffic Sign
Detection for
Vision-based
Driver’s
Assistance
Prepared By: HUDA SEYAM
Date: 5/17/2021
in Land-based
Vehicles

2.  Purpose
 Proposed algorithm
 color enhancement
 color segmentation
 edge detector
 Hough transform
OUTLINE
 Experimental results
 Findings
 Challenges & limitation
 Future work

3. Proposed an algorithm that detect the road signs in the
images based on color detection followed by shape
detection.
Detect road signs in the images that were taken by a low-
resolution camera mounted in front of a moving car. The
algorithm work in two types of road signs the yellow warning
signs and red stop signs.
PURPOSE

4. PROPOSED ALGORITHM
STEP 01
Color Enhancement
STEP 04
Hough Transform
STEP 02
Color Segmentation
STEP 03
Edge Detector

5. Image taken
from a front-
viewing
camera
mounted on a
moving car

6. COLOR ENHANCEMENT
Color enhancement to increase the contrast of the colors
and prepare it for color space transformation.
● Transform the image from RGB space model to Lab
space.
● Apply histogram equalization on Just (L) luminance or
lightness layer of Lab model
● Transform the processed image back to RGB space.
Output: Enhanced Image

7. COLOR SEGMENTATION
Color segmentation according to a target color.
● Transform the image into desired color space
● Extract the color by thresholding
● Return a binary mask of the color regions.
Output: binary image containing labeled color regions.

8. EDGE DETECTOR
Edge detector applied to each color region.
● Perform region counting and labelling on the binary
image.
● Extract a rectangular region for each of the labelled
region.
● Apply canny edge detector on each of the extracted
grayscale image region.
Output: binary image containing edge for each detected
region.

9. HOUGH TRANSFORM
Hough transform is then performed to determine the
shape of the road sign.
● Apply Hough transform on the edge image.
● Compute a distance score between the distribution of
peaks and a target shape distribution to determine if
there is a desired shape present
Output: Hough transform with peaks for each detected
region

10. Detection result as
a region
containing a road
sign depend on the
distribution of
peaks in Hough
transform.

11. EXPERIMENTAL RESULTS
Test Set Correct detection Incorrect detection
Diamond-shaped yellow
warning signs
6/10 4/10
Red stop signs 8/10 2/10
 Tow testing set with different illumination condition and several quality
resolutions.
 The location and size of the road signs also different form image to image.

12.  Experimental results show that
The color-based detection is sensitive
to illumination conditions
The shape detection is sensitive to
the
complexity of the background.
FINDING
S

13. Challenges
 The complexity of background creates huge error in shape detection because it has a
great number of outliers in Hough transform.
 The low-resolution image effects the edge extraction.
Limitation
 The algorithm requires a priori knowledge of the color and shape of the traffic sign
being detected.
 The algorithm will not be able to tell if the given information is incorrect.
CHALLENGES & LIMITATION

14. FUTURE WORK
Improving the detection accuracy by
using Machine-learning techniques

15. Reference
Zhang, S. (2016). Traffic sign detection for
vision-based driver’s assistance in land-based
vehicles. Technical report, School of
Aeronautics and Astronautics-Stanford
University.

16. CREDITS: This presentation template was created by Slidesgo,
including icons by Flaticon, and infographics & images by Freepik
THANKS A
LOT!
Do you have any questions?