This document summarizes a study analyzing sign detection using LiDAR technology on highways I-75, I-85, and I-20 in Georgia. Researchers established a ground truth dataset by manually inventorying over 3,800 signs across the three highways. They then tested LiDAR-based sign detection by comparing results to the ground truth under different parameter settings. Adjusting parameters like sensitivity and minimum hit count improved detection accuracy, identifying 465 signs on I-20 Eastbound compared to 408 signs previously. However, limitations included some inaccurate ground truth locations and inability to distinguish clustered signs. The study aimed to refine sign detection to better inform transportation agencies' sign management.

1. Study of Sign
Characteristics on
I-75, I-85, and I-
20
Morris Smith
CEE 2699
Spring 2014
Dr. Yichang Tsai

2. Contents
1. Introduction
i. Importance of Signs
ii. Sign Management
2. Objective
3. Ground Truth Establishment
i. Methodology for Sign Data
Extraction
ii. Methodology for Sign Data
QA/QC
4. Ground Truth Summary
i. Statistics
ii. Observations
5. LiDAR-Based Sign Detaction
i. Methodology
ii. Observations
iii. Limitations

3. Introduction – Importance of Signs
• Traffic regulation and guidance
• Symbols that are simple and easy to comprehend
• Safety
• Especially important on freeways and highways
• Infrastructure development
• Travel efficiency increased

4. Introduction – Sign Management
• Traffic sign databases must be updated on
a regular basis
• Understanding several correlations involving
sign type and location
• Provides information to determine sign
placement/replacement
• Advances in technology allow for quicker and
broader analysis

5. Objective
• To assess and improve the effectiveness of using LiDAR
technology for sign detection
• Establish a ground truth as a control for experimentation
• Fine-tune key parameters in LiDAR-based sign detection and record
observations
• Based on observations, make recommendations to improve the current LiDAR-
based sign detection method

6. Ground Truth Establishment – Methodology for Sign
Data Extraction
• Inventory signs along three major interstate highways in Georgia
using Trimble Trident Analyst for Spatial Imaging software to
designate GPS coordinates
• Construction signs, overhead signs, informatory signs, mile markers,
cautionary signs, street signs
• NOT inventoried: Temporary signs, guardrail markers, electronic signs, signs
on right-of-way on ramp
• Morris: I-20 East and West
• Alabama – Atlanta and Atlanta – South Carolina

7. Types of Signs Inventoried on I-20

8. Ground Truth Summary – Statistics
• I-20
• Total number of signs inventoried: 3,847
• Total number of frames: 136,016
West of
Atlanta
East of
Atlanta
Eastbound 631 1,251
Westbound 589 1,371
Total 1,220 2,622
Number of Signs Inventoried on I-20

9. Ground Truth Establishment – Methodology for Sign
Data QA/QC
• Perform cross-checks with other researchers who extracted data on
other highways
• Ensures consistency among datasets
• Morris – reviewed Yutong’s data on I-75 North and South
• Florida – Atlanta and Atlanta – Tennessee
• Observations
• Dataset is considerably larger than that of I-20
• Many temporary signs that were erected during construction were inventoried

10. 81,288 frames
QA/QC – Observations

11. Ground Truth Summary – Observations
• Overpass street signs were difficult to accurately inventory
• Sign markers for the highway in one direction appeared inaccurate
when travelling in opposite direction
• Some signs were blocked by large passing vehicles – these
coordinates are approximated

12. Ground Truth Summary – Observations(2)

13. LiDAR-Based Sign Detection
• Experimented with given parameters and ran LiDAR detection tool
• Compared results (sign location, number of signs inventoried, etc.)
with collected ground truth data
• Based on comparison results, parameters were refined to achieve
the best set for optimal sign inventory performance

14. LiDAR-Based Sign Detection – Methodology
• The only parameters that
were considered were the
first five in the given
window
• The other parameters were
left alone
• Compared results via
visual analysis with ground
truth and other sets
• Used I-20 Eastbound,
West of Atlanta to test
results

15. LiDAR-Based Sign Detection – Observations
Sensitivity 0.70
Minimum Elevation 2 ft.
Min. Dist. Sign to Sign 12 ft.
Max Lateral Distance 100 ft.
Minimum Hit Count 10
Number of Signs 408 signs
• Many signs that had multiple
detections in previous dataset
had only one marker in this set
• Used initially established dataset as control on which to improve
• Picked up a high percentage of
ground signs
• Many signs were detected multiple
times, e.g. contained multiple markers
*only the vertical camera playback was entered
when testing this dataset
Sensitivity 0.70
Minimum Elevation 2 ft.
Min. Dist. Sign to Sign 12 ft.
Max Lateral Distance 100 ft.
Minimum Hit Count 15
Number of Signs 465 signs

16. LiDAR-Based Sign Detection – Observations(2)

17. LiDAR-Based Sign Detection – Limitations
• Limited time to test parameters
• Many ground truth markers were not completely accurate in
locating the center of the sign
• Sign locations with more than one sign (i.e. I-20 signs with direction
sign) were technically counted as one sign with LiDAR detection
• Some stationary trucks were detected by LiDAR cameras
• LiDAR only inventoried signs on the right-hand camera

18. Questions?