Traffic signs recognition with deep learning

Yasmina, Djebbara; Rebai, Karima; Azouaoui, Ouahiba

doi:10.1109/icass.2018.8652024

Cited by 21 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to build a successful traffic sign classification system, many researchers used the LeNet-5 model in their work [27,34,35,43,44]. Our improvements were very successful, and the accuracy of our model was the best as shown in Table 6.…”

Section: Journal Of Sensorsmentioning

confidence: 86%

“…These features are called low-level features. For example, in this paper [35], the authors made a modification on the first layer of the fully connected network by adding the results of the first convolution operation since the first convolution characteristics can contain elements as important as those injected in the fully connected network. According to our method, we choose to have two successive convolutional layers before the pooling layers, in order to be able to build better data representations without quickly losing all your spatial information.…”

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%

See 2 more Smart Citations

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

Section: Journal Of Sensorsmentioning

confidence: 86%

Section: Enhanced Lenet-5mentioning

confidence: 99%

See 1 more Smart Citation

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

2021

View full text Add to dashboard Cite

show abstract

“…Deep Learning models and applications have been used in tasks such as image classification, [21][22][23] document analysis and text recognition, [24][25][26] natural language processing, [27][28][29] and video analysis [30][31][32] in industries ranging from automated driving to medical devices as shown in Figure 3. In References 35-37, the authors investigated the use of visual information to detect and interpret road signs using hierarchical classifier structures that combine Support Vector Machines (SVM) for image verification and Convolutional Neural Networks (CNN) for final recognition.…”

Section: Applications Of Deep Learningmentioning

confidence: 99%

Deep learning support for intelligent transportation systems

Ibáñez

Contreras-Castillo

Zeadally

2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Intelligent Transportation Systems (ITS) help improve the ever-increasing vehicular flow and traffic efficiency in urban traffic to reduce the number of accidents. The generation of massive amounts of data generated by all the digital devices connected to the transportation network enables the creation of datasets to perform an in-depth analysis of the data using deep learning methods. Such methods can help predict traffic performance, automated traffic light management, lane detection, and identifying objects near vehicles to increase the safety and efficiency of ITS. We discuss some of the challenges that need to be solved to achieve seamless integration between ITS and deep learning methods to address issues such as (1) improving traffic flow/transportation logistics, (2) predicting best routes for the transportation of goods, (3) optimal fuel consumption, (4) intelligent environmental conditions perception, (5) traffic speed management, and accident prevention.

show abstract

“…For images, the geometric transformation of the training data such as rotation, translation, and shear has shown an improvement for classification tasks [20]. In [21], geometric translations and dropout layers are utilized to improve traffic sign recognition. The results indicate that the validation accuracy was improved by more than 5% considering rotation, translation, and shearing augmentation methods.…”

Section: Introductionmentioning

confidence: 99%

Event Augmentation for Contact Force Measurements

et al. 2022

View full text Add to dashboard Cite

Neuromorphic vision sensor is an attractive technology that offers high dynamic range, and low latency which are crucial in robotic applications. However, the lack of event-based data in this field, limits the sensors' performance in a real-world environments. In this paper, we propose a novel augmentation technique for neuromorphic vision sensors to improve contact force measurements from events. The proposed method shifts a proportion of events across the time domain, 'Temporal Event Shifting', to augment the dataset. A new set of grasping experiments is performed to validate and analyze the effectiveness of the proposed augmentation method for contact force measurements. The results indicate that temporal event shifting is highly effective augmentation method which improves the models' accuracy for the contact force estimation by thirty percent without performing new experiments. INDEX TERMSEvent-based augmentation, Neuromorphic augmentation, Vision-based Tactile sensor.

show abstract

Traffic signs recognition with deep learning

Cited by 21 publications

References 7 publications

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

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

Deep learning support for intelligent transportation systems

Event Augmentation for Contact Force Measurements

Contact Info

Product

Resources

About