Traffic sign recognition based on deep convolutional neural network

Yin, Shi-hao; Jun, Deng; Zhang, Dawei; Du, Jing-yuan

doi:10.1007/s11801-017-7209-0

Cited by 12 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where a computer system imitates the visual cortex and brain to identify patterns from visual elements [ 27 ]. CNNs have been used in multiple fields, such as medical [ 28 , 29 , 30 , 31 ], autonomous vehicles [ 32 , 33 , 34 ], and agricultural [ 35 , 36 ], just to name a few. A CNN differs from a conventional neural network (NN) because it contains at least a convolutional layer.…”

Section: Methodsmentioning

confidence: 99%

Fuzzy Edge-Detection as a Preprocessing Layer in Deep Neural Networks for Guitar Classification

Torres

González

Martínez

2022

Sensors

View full text Add to dashboard Cite

Deep neural networks have demonstrated the capability of solving classification problems using hierarchical models, and fuzzy image preprocessing has proven to be efficient in handling uncertainty found in images. This paper presents the combination of fuzzy image edge-detection and the usage of a convolutional neural network for a computer vision system to classify guitar types according to their body model. The focus of this investigation is to compare the effects of performing image-preprocessing techniques on raw data (non-normalized images) with different fuzzy edge-detection methods, specifically fuzzy Sobel, fuzzy Prewitt, and fuzzy morphological gradient, before feeding the images into a convolutional neural network to perform a classification task. We propose and compare two convolutional neural network architectures to solve the task. Fuzzy edge-detection techniques are compared against their classical counterparts (Sobel, Prewitt, and morphological gradient edge-detection) and with grayscale and color images in the RGB color space. The fuzzy preprocessing methodologies highlight the most essential features of each image, achieving favorable results when compared to the classical preprocessing methodologies and against a pre-trained model with both proposed models, as well as achieving a reduction in training times of more than 20% compared to RGB images.

show abstract

Section: Methodsmentioning

confidence: 99%

Fuzzy Edge-Detection as a Preprocessing Layer in Deep Neural Networks for Guitar Classification

Torres

González

Martínez

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…For the sake of enhancing the rate of model learning, this article uses image normalization processing, so that make the mean of each input data close to 0 or the difference between it and its mean square error is very small [14] . Use the mean of the dataset minus the pixel values of the image and divide by its standard deviation to get the center of the dataset distribution.…”

Section: Image Normalizationmentioning

confidence: 99%

Traffic sign recognition based on improved VGG16 algorithm

Wang,

Jiang,

Zhou

2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

View full text Add to dashboard Cite

The detection and recognition of traffic signs acts an extremely crucial role in driverless technology and assisted driving systems. In view of the low recognition rate of traffic signs, this paper improves the neural network based on the existing VGG16 network architecture, including adding BN layer, adopting batch normalization algorithm and adding attention mechanism. The GTSRB data set is enhanced by scaling and random rotation. The data is preprocessed by image graying and image normalization. In this paper, the model recognition rate reached 99.34%, which is of great value to enhance the recognition accuracy of traffic signs and promote the development of intelligent transportation.

show abstract

“…For this reason, the ReLU function is chosen which prevents the problem of the ReLU dying: this variation of the ReLU has a slight positive slope in the negative area, which allows backward propagation even with negative input values. For example, in this work [37], the authors compared ReLU and LeakyReLU according to the classification accuracy on the GTSRB database. The effect is excellent when using LeakyReLU.…”

Section: Enhanced Lenet-5mentioning

confidence: 99%

“…Optimized LeNet-5 [34] Between 93 and 96 Modified LeNet-5 network [35] 95.2 Improved LeNet-5 [27] 99.75 LeNet architecture [45] 96.23 Small CNN [46] 97.4 Improved LeNet-5 [44] 98.12 Lightweight deep network [43] 99.61 Deep CNN [37] 98.96 Efficient CNN [41] 99.66 Ours 99.84…”

Section: Accuracy (%)mentioning

confidence: 99%

A Lightweight Model for Traffic Sign Classification Based on Enhanced LeNet‐5 Network

2021

View full text Add to dashboard Cite

For several years, much research has focused on the importance of traffic sign recognition systems, which have played a very important role in road safety. Researchers have exploited the techniques of machine learning, deep learning, and image processing to carry out their research successfully. The new and recent research on road sign classification and recognition systems is the result of the use of deep learning-based architectures such as the convolutional neural network (CNN) architectures. In this research work, the goal was to achieve a CNN model that is lightweight and easily implemented for an embedded application and with excellent classification accuracy. We choose to work with an improved network LeNet-5 model for the classification of road signs. We trained our model network on the German Traffic Sign Recognition Benchmark (GTSRB) database and also on the Belgian Traffic Sign Data Set (BTSD), and it gave good results compared to other models tested by us and others tested by different researchers. The accuracy was 99.84% on GTSRB and 98.37% on BTSD. The lightness and the reduced number of parameters of our model (0.38 million) based on the enhanced LeNet-5 network pushed us to test our model for an embedded application using a webcam. The results we found are efficient, which emphasize the effectiveness of our method.

show abstract

Traffic sign recognition based on deep convolutional neural network

Cited by 12 publications

References 5 publications

Fuzzy Edge-Detection as a Preprocessing Layer in Deep Neural Networks for Guitar Classification

Fuzzy Edge-Detection as a Preprocessing Layer in Deep Neural Networks for Guitar Classification

Traffic sign recognition based on improved VGG16 algorithm

A Lightweight Model for Traffic Sign Classification Based on Enhanced LeNet‐5 Network

Contact Info

Product

Resources

About