Evaluation Method of Deep Learning-Based Embedded Systems for Traffic Sign Detection

Traffic Sign Detection (TSD) is a complex and fundamental task for developing autonomous vehicles; it is one of the most critical visual perception problems since failing in this task may cause accidents. This task is fundamental in decision-making and involves different internal conditions such as...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.101217-101238
Main Authors: Lopez-Montiel, Miguel, Orozco-Rosas, Ulises, Sanchez-Adame, Moises, Picos, Kenia, Ross, Oscar Humberto Montiel
Format: Article
Language:English
Subjects:
Algorithms
Autonomous vehicles
computer vision
Decision making
Deep learning
digital systems
Embedded systems
Feature extraction
Graphics processing units
Hardware
Hardware acceleration
Machine learning
Real-time systems
Statistical analysis
Task analysis
Traffic sign detection
Traffic signs
Training
Visual perception
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Summary:Traffic Sign Detection (TSD) is a complex and fundamental task for developing autonomous vehicles; it is one of the most critical visual perception problems since failing in this task may cause accidents. This task is fundamental in decision-making and involves different internal conditions such as the internal processing system or external conditions such as weather, illumination, and complex backgrounds. At present, several works are focused on the development of algorithms based on deep learning; however, there is no information on a methodology based on descriptive statistical analysis with results from a solid experimental framework, which helps to make decisions to choose the appropriate algorithms and hardware. This work intends to cover that gap. We have implemented some combinations of deep learning models (MobileNet v1 and ResNet50 v1) in a combination of the Single Shot Multibox Detector (SSD) algorithm and the Feature Pyramid Network (FPN) component for TSD in a standardized dataset (LISA), and we have tested it on different hardware architectures (CPU, GPU, TPU, and Embedded System). We propose a methodology and the evaluation method to measure two types of performance. The results show that the use of TPU allows achieving a processing training time 16.3 times faster than GPU and better results in terms of precision detection for one combination.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3097969