Evaluation of Deep Learning Algorithms for Traffic Sign Detection to Implement on Embedded Systems

Lopez-Montiel, Miguel; Orozco-Rosas, Ulises; Sánchez-Adame, Moises; Picos, Kenia; Montiel, Oscar

doi:10.1007/978-3-030-58728-4_5

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…In this article, we continue our previous works [16], [17] for the traffic sign detection problem. We propose a comparative evaluation of two different hardware accelerators to measure the detection system's performance on traffic sign detection.…”

Section: Introductionsupporting

confidence: 52%

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

Lopez-Montiel¹,

Orozco-Rosas

Sánchez-Adame³

et al. 2021

IEEE Access

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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.

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Section: Introductionsupporting

confidence: 52%

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

Lopez-Montiel¹,

Orozco-Rosas

Sánchez-Adame³

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

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A novel hybrid machine learning approach for traffic sign detection using CNN-GRNN

Pandurangan

Jayaseelan²,

Rajalingam

et al. 2023

IFS

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The traffic signal recognition model plays a significant role in the intelligent transportation model, as traffic signals aid the drivers to driving the more professional with awareness. The primary goal of this paper is to proposea model that works for the recognition and detection of traffic signals. This work proposes the pre-processing and segmentation approach applying machine learning techniques are occurred recent trends of study. Initially, the median filter & histogram equalization technique is utilized for pre-processing the traffic signal images, and also information of the figures being increased. The contrast of the figures upgraded, and information about the color shape of traffic signals are applied by the model. To localize the traffic signal in the obtained image, then this region of interest in traffic signal figures are extracted. The traffic signal recognition and classification experiments are managed depending on the German Traffic Signal Recognition Benchmark-(GTSRB). Various machine learning techniques such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), Convolutional neural network (CNN)- General Regression Neural Network (GRNN) is used for the classification process. Finally, the obtained results will be compare in terms of the performance metrics like accuracy, F1 score, kappa score, jaccard score, sensitivity, specificity, recall, and precision. The result shows that CNN-GRNN with ML techniques by attaining 99.41% accuracy compare to other intelligent methods. In this proposed technique is used for detecting and classifying various categories of traffic signals to improve the accuracy and effectiveness of the system.

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Evaluation of Deep Learning Algorithms for Traffic Sign Detection to Implement on Embedded Systems

Cited by 2 publications

References 41 publications

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

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

A novel hybrid machine learning approach for traffic sign detection using CNN-GRNN

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