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A Fusion Approach to Detect Traffic Signs Using Registered Color Images and Noisy Airborne LiDAR Data
- Source :
- Applied Sciences, Volume 11, Issue 1, Applied Sciences, Vol 11, Iss 309, p 309 (2021)
- Publication Year :
- 2020
- Publisher :
- MDPI AG, 2020.
-
Abstract
- Traffic sign detection is considered as one of the active research topics in transportation and computer vision. The previous works mainly focus on detecting traffic signs in images or in mobile light detection and ranging (LiDAR) data. In this paper, we propose a novel deep learning method to accurately detect traffic signs by fusing the complementary features from registered airborne geo-referenced color images and noisy airborne LiDAR data. Specifically, we first segment the airborne color images to road and non-road segments by integrating various local features in an inequality constraint quadratic optimization model. Next, we find the corresponding road regions in LiDAR data and extract high elevated objects above the road. We then segment the extracted objects to different regions corresponding to traffic sign candidates using Euclidean distance-based clustering. Finally, we find the corresponding traffic sign candidates in color images, extract their deep features, and represent them in a convex optimization model to classify the candidates. A set of extensive experiments have been carried out on the airborne geo-referenced color images and noisy airborne LiDAR data captured by Utah State University from I-15 highway. The results show the effectiveness of the proposed method in detecting traffic signs.
- Subjects :
- Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
lcsh:Technology
Convolutional neural network
lcsh:Chemistry
0502 economics and business
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
Computer vision
Cluster analysis
lcsh:QH301-705.5
Instrumentation
Fluid Flow and Transfer Processes
data fusion
050210 logistics & transportation
lcsh:T
business.industry
Process Chemistry and Technology
Convolutional Neural Networks
05 social sciences
General Engineering
Ranging
Sensor fusion
lcsh:QC1-999
Computer Science Applications
Euclidean distance
Lidar
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
traffic sign detection
020201 artificial intelligence & image processing
Artificial intelligence
lcsh:Engineering (General). Civil engineering (General)
business
Focus (optics)
Traffic sign
lcsh:Physics
Subjects
Details
- ISSN :
- 20763417
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Applied Sciences
- Accession number :
- edsair.doi.dedup.....ab08c382b876a3d03294b4248734e15a