scholarly journals A Lightweight Model for Traffic Sign Classification Based on Enhanced LeNet-5 Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ameur Zaibi ◽  
Anis Ladgham ◽  
Anis Sakly
Keyword(s):  
Deep Learning ◽  
Research Work ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Data Set ◽  
Road Signs ◽  
Sign Recognition ◽  
Road Sign ◽  
Recognition Systems ◽  
Embedded Application

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.

scholarly journals Exploiting synergies of mobile mapping sensors and deep learning for traffic sign recognition systems

2017 ◽  
Vol 89 ◽  
pp. 286-295 ◽  
Author(s):  
Álvaro Arcos-García ◽  
Mario Soilán ◽  
Juan A. Álvarez-García ◽  
Belén Riveiro
Keyword(s):  
Deep Learning ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Sign Recognition ◽  
Recognition Systems

scholarly journals Modification of the Pre-Processing Stage of a Traffi Sign Recognition System Taking into Account the Characteristics of the Subject Area

Vestnik NSUEM ◽  
2020 ◽  
pp. 235-249
Author(s):  
S. Yu. Pchelintsev
Keyword(s):  
Processing Speed ◽  
Recognition System ◽  
Subject Area ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Road Signs ◽  
Sign Recognition ◽  
The Subject ◽  
Recognition Systems ◽  
High Level

Traffic sign recognition systems require a high level of responsiveness and accuracy with limited use of computing resources. The process of image pre-processing precedes the process of directly recognizing images, therefore, the recognition results depend on its effectiveness. When conducting pre-processing, it is important to take into account the features of the subject area, within which recognition is performed. The article discusses the process of pre-processing and preparing images in the context of creating a system for recognizing road signs. The main problems that arise during the operation of such a system are identified. Their solutions are proposed. Own combination of these solutions allowed us to create a new system for recognizing road signs, which gives a gain in processing speed by cutting off images of no interest before entering the classifier, and also taking into account the peculiarities of operation in an urban environment – more difficult conditions compared with recognition of road signs on tracks or on artificially created training grounds.

scholarly journals Cautionary Sign Analysis of Traffic Sign Data-Set Using Supervised Spiking Neuron Technique

2018 ◽  
Vol 7 (3.14) ◽  
pp. 233
Author(s):  
Mohd Safirin Karis ◽  
Nursabillilah Mohd Ali ◽  
Nur Aisyah Abdul Ghafor ◽  
Muhamad Aizuddin Akmal Che Jusoh ◽  
Nurasmiza Selamat ◽  
...  
Keyword(s):  
Mean Value ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Data Set ◽  
Traffic Signs ◽  
Recognition Process ◽  
Sign Recognition ◽  
Anticlockwise Rotation ◽  
The Impact ◽  
Detection And Recognition

In this paper, 19 cautionary traffic signs were selected as a database and 3 types of conditions have been proposed. The conditions are 5 different time of image taken; hidden region and anticlockwise rotation are all the experiments design that will shows all the errors in producing the it’s mean value and the performance of traffic sign recognition. Initial hypothesis was made as the error will become larger as the interruption getting bigger. Based on the results of the five-different time of image taken, the error gives the best performance; less error when time is between 8am to 12am due to the brightness factors and the sign can be recognize clearly during noon session. The hidden region conditions show good performances of the detection and recognition of the system depend on the lesser coverage of the hidden region introduce on traffic sign because if the hidden region coverage is huge the database will get confuse and take a longer time to do the recognition process. Lastly, in anticlockwise rotation shows that 90o gave large value of error causing the system unable to recognize sign perfectly rather than 135o angle. To sum-up, detection and recognition process are not depending on higher number of angle but the process solely depending on their value of sample for each traffic signs. The error will give the impact towards traffic sign recognition and detection process. In conclusion, SNN can perform the detection and recognition process to all objects as in the future the system will become more stable with the right technique on spiking models and well-developed technology in this field.  

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