RIECNN: real-time image enhanced CNN for traffic sign recognition

Abdel-Salam, Reem; Mostafa, Rana; Abdel-Gawad, Ahmed H.

doi:10.1007/s00521-021-06762-5

Cited by 31 publications

(5 citation statements)

References 12 publications

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“…Reem Abdel-Salam et al ( 2022) [18] proposed a Real-Time Image Enhancement Convolutional Neural Network (RIECNN) in the hope that the method would address the effect of environmental factors on traffic sign recognition and compensate for the lack of real-time performance of deep learning algorithms. In preprocessing, they used image contrast enhancement, multi-scale Retinex algorithm, histogram equalization and edge enhancement.…”

Section: Related Workmentioning

confidence: 99%

E-MobileViT: A Lightweight Model for Traffic Sign Recognition

Song,

Ye,

Manoharan

2024

Preprint

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Traffic sign recognition is crucial for intelligent transportation and autonomous driving, ensuring road safety and efficient traffic management. In this paper, a lightweight enhanced MobileViT model (E-MobileViT) is proposed. It is based on the MobileViT model, combining the advantages of CNN and Transformer. We integrate Efficient Local Attention (ELA) and Convolutional Block Attention Module (CBAM) mechanisms in the model to improve feature extraction. The proposed model improves the feature fusion structure, and significantly reduces the number of model parameters. We evaluated the model on German Traffic Sign Recognition Benchmark (GTSRB), Belgian Traffic Signs Database (BTSD) and China Traffic Signs Database (TSRD) datasets, and its accuracy reaches 99.61%, 99.26% and 97.34%, respectively, which outperforms traditional and advanced models. We confirmed the key role of ELA and CBAM mechanisms through ablation experiments. With fewer parameters than mainstream models, our E-MobileViT model is suitable for resource-constrained environments such as mobile devices, providing a balanced solution for traffic sign recognition tasks.

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Section: Related Workmentioning

confidence: 99%

E-MobileViT: A Lightweight Model for Traffic Sign Recognition

Song,

Ye,

Manoharan

2024

Preprint

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“…In [ 52 ], the authors found that the Tiny-YOLOv2 network is fast but outperformed by YOLOv2 or YOLOv3 deep learners. While the authors of [ 53 ] introduced real time image enhancement CNN and achieved an accuracy of 99.25% for the BelgiumTSC, 99.75% for GTSRB, and 99.55% for Croatian Traffic Sign (rMASTIF). Authors of [ 54 ], developed a real time TSR by using the You Only Look Once (YOLO) algorithm to train the model for Malaysian traffic sign recognition and tested it on five types of warning traffic signs.…”

Section: Background On Traffic Sign Recognitionmentioning

confidence: 99%

Towards Enhancing Traffic Sign Recognition through Sliding Windows

Atif¹,

Zoppi²,

Gharib

et al. 2022

Sensors

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Automatic Traffic Sign Detection and Recognition (TSDR) provides drivers with critical information on traffic signs, and it constitutes an enabling condition for autonomous driving. Misclassifying even a single sign may constitute a severe hazard, which negatively impacts the environment, infrastructures, and human lives. Therefore, a reliable TSDR mechanism is essential to attain a safe circulation of road vehicles. Traffic Sign Recognition (TSR) techniques that use Machine Learning (ML) algorithms have been proposed, but no agreement on a preferred ML algorithm nor perfect classification capabilities were always achieved by any existing solutions. Consequently, our study employs ML-based classifiers to build a TSR system that analyzes a sliding window of frames sampled by sensors on a vehicle. Such TSR processes the most recent frame and past frames sampled by sensors through (i) Long Short-Term Memory (LSTM) networks and (ii) Stacking Meta-Learners, which allow for efficiently combining base-learning classification episodes into a unified and improved meta-level classification. Experimental results by using publicly available datasets show that Stacking Meta-Learners dramatically reduce misclassifications of signs and achieved perfect classification on all three considered datasets. This shows the potential of our novel approach based on sliding windows to be used as an efficient solution for TSR.

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“…Traffic sign recognition has attracted widespread interest, and many related methods have been proposed [4][5][6] to address it. Before the era of deep learning, several studies used genetic algorithms [7] or shallow neural networks to perform traffic sign recognition [8,9].…”

Section: Introductionmentioning

confidence: 99%

Zero-Shot Traffic Sign Recognition Based on Midlevel Feature Matching

Gan,

Li,

Togo

et al. 2023

Sensors

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Traffic sign recognition is a complex and challenging yet popular problem that can assist drivers on the road and reduce traffic accidents. Most existing methods for traffic sign recognition use convolutional neural networks (CNNs) and can achieve high recognition accuracy. However, these methods first require a large number of carefully crafted traffic sign datasets for the training process. Moreover, since traffic signs differ in each country and there is a variety of traffic signs, these methods need to be fine-tuned when recognizing new traffic sign categories. To address these issues, we propose a traffic sign matching method for zero-shot recognition. Our proposed method can perform traffic sign recognition without training data by directly matching the similarity of target and template traffic sign images. Our method uses the midlevel features of CNNs to obtain robust feature representations of traffic signs without additional training or fine-tuning. We discovered that midlevel features improve the accuracy of zero-shot traffic sign recognition. The proposed method achieves promising recognition results on the German Traffic Sign Recognition Benchmark open dataset and a real-world dataset taken from Sapporo City, Japan.

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RIECNN: real-time image enhanced CNN for traffic sign recognition

Cited by 31 publications

References 12 publications

E-MobileViT: A Lightweight Model for Traffic Sign Recognition

E-MobileViT: A Lightweight Model for Traffic Sign Recognition

Towards Enhancing Traffic Sign Recognition through Sliding Windows

Zero-Shot Traffic Sign Recognition Based on Midlevel Feature Matching

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