scholarly journals A Thorough Evaluation of Kernel Order in CNN Based Traffic Signs Recognition

2020 ◽  
Author(s):  
Lucas De Oliveira ◽  
Guilherme Mota ◽  
Vitor Vidal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Analysis ◽  
Statistical Significance ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
Stochastic Characteristics ◽  
The Impact

Convolutional Neural Network is an important deep learning architecture for computer vision. Alongside with its variations, it brought image analysis applications to a new performance level. However, despite its undoubted quality, the evaluation of the performance presented in the literature is mostly restricted to accuracy measurements. So, considering the stochastic characteristics of neural networks training and the impact of the architectures configuration, research is still needed to affirm if such architectures reached the optimal configuration for their focused problems. Statistical significance is a powerful tool for a more accurate experimental evaluation of stochastic processes. This paper is dedicated to perform a thorough evaluation of kernel order influence over convolutional neural networks in the context of traffic signs recognition. Experiments for distinct kernels sizes were performed using the most well accepted database, the socalled German Traffic Sign Recognition Benchmark.

scholarly journals Energy-Efficient Gabor Kernels in Neural Networks with Genetic Algorithm Training Method

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 105 ◽  
Author(s):  
Fanjie Meng ◽  
Xinqing Wang ◽  
Faming Shao ◽  
Dong Wang ◽  
Xia Hua
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Energy Efficient ◽  
Experimental Result ◽  
Training Method ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Deep Model

Deep-learning convolutional neural networks (CNNs) have proven to be successful in various cognitive applications with a multilayer structure. The high computational energy and time requirements hinder the practical application of CNNs; hence, the realization of a highly energy-efficient and fast-learning neural network has aroused interest. In this work, we address the computing-resource-saving problem by developing a deep model, termed the Gabor convolutional neural network (Gabor CNN), which incorporates highly expression-efficient Gabor kernels into CNNs. In order to effectively imitate the structural characteristics of traditional weight kernels, we improve upon the traditional Gabor filters, having stronger frequency and orientation representations. In addition, we propose a procedure to train Gabor CNNs, termed the fast training method (FTM). In FTM, we design a new training method based on the multipopulation genetic algorithm (MPGA) and evaluation structure to optimize improved Gabor kernels, but train the rest of the Gabor CNN parameters with back-propagation. The training of improved Gabor kernels with MPGA is much more energy-efficient with less samples and iterations. Simple tasks, like character recognition on the Mixed National Institute of Standards and Technology database (MNIST), traffic sign recognition on the German Traffic Sign Recognition Benchmark (GTSRB), and face detection on the Olivetti Research Laboratory database (ORL), are implemented using LeNet architecture. The experimental result of the Gabor CNN and MPGA training method shows a 17–19% reduction in computational energy and time and an 18–21% reduction in storage requirements with a less than 1% accuracy decrease. We eliminated a significant fraction of the computation-hungry components in the training process by incorporating highly expression-efficient Gabor kernels into CNNs.

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.  

Deep learning–based traffic sign recognition for unmanned autonomous vehicles

2018 ◽  
Vol 232 (5) ◽  
pp. 497-505 ◽  
Author(s):  
Di Zang ◽  
Zhihua Wei ◽  
Maomao Bao ◽  
Jiujun Cheng ◽  
Dongdong Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
The Time Domain

Being one of the key techniques for unmanned autonomous vehicle, traffic sign recognition is applied to assist autopilot. Colors are very important clues to identify traffic signs; however, color-based methods suffer performance degradation in the case of light variation. Convolutional neural network, as one of the deep learning methods, is able to hierarchically learn high-level features from the raw input. It has been proved that convolutional neural network–based approaches outperform the color-based ones. At present, inputs of convolutional neural networks are processed either as gray images or as three independent color channels; the learned color features are still not enough to represent traffic signs. Apart from colors, temporal constraint is also crucial to recognize video-based traffic signs. The characteristics of traffic signs in the time domain require further exploration. Quaternion numbers are able to encode multi-dimensional information, and they have been employed to describe color images. In this article, we are inspired to present a quaternion convolutional neural network–based approach to recognize traffic signs by fusing spatial and temporal features in a single framework. Experimental results illustrate that the proposed method can yield correct recognition results and obtain better performance when compared with the state-of-the-art work.

scholarly journals CNN Based Approach for Traffic Sign Recognition System

2021 ◽  
Vol 11 (1) ◽  
pp. 23-33
Author(s):  
Karan Singh ◽  
Nikita Malik
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Real Life ◽  
Recognition System ◽  
Traffic Sign Recognition ◽  
Driver Assistance Systems ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition

Machine Learning (ML) involves making a machine able to learn and take decisions on real-life problems by working with an efficient set of algorithms. The generated ML models find application in different areas of research and management. One such field, automotive technology, employs ML enabled commercialized advanced driver assistance systems (ADAS) which include traffic sign recognition as a part. With the increasing demand for the intelligence of vehicles, and the advent of self-driving cars, it is extremely necessary to detect and recognize traffic signs automatically through computer technology. For this, neural networks can be applied for analyzing images of traffic signs for cognitive decision making by autonomous vehicles. Neural networks are the computing systems which act as a means of performing ML. In this work, a convolutional neural network (CNN) based ML model is built for recognition of traffic signs accurately for decision making, when installed in driverless vehicles.

scholarly journals Traffic sign recognition using convolutional neural networks

2021 ◽  
Vol 9 (2) ◽  
pp. 120-125
Author(s):  
Mutaqin Akbar
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Stochastic Gradient Descent ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
Training Stage ◽  
Testing Stage ◽  
Fully Connected

Traffic sign recognition (TSR) can be used to recognize traffic signs by utilizing image processing. This paper presents traffic sign recognition in Indonesia using convolutional neural networks (CNN). The overall image dataset used is 2050 images of traffic signs, consisting of 10 kinds of signs. The CNN layer used in this study consists of one convolution layer, one pooling layer using maxpool operation, and one fully connected layer. The training algorithm used is stochastic gradient descent (SGD). At the training stage, using 1750 training images, 48 filters, and a learning rate of 0.005, the recognition results in 0.005 of loss and 100 % of accuracy. At the testing stage using 300 test images, the system recognizes the signs with 0.107 of loss and 97.33 % of accuracy.

scholarly journals Traffic Sign Classification and Detection of Indian Traffic Signs using Deep Learning

2021 ◽  
pp. 215-219
Author(s):  
Manjiri Bichkar ◽  
Suyasha Bobhate ◽  
Prof. Sonal Chaudhari
Keyword(s):  
Classification Model ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Electric Cars ◽  
Blob Detection ◽  
Sign Recognition ◽  
Testing Dataset ◽  
Self Driving Cars ◽  
Traffic Sign Classification

This paper presents an effective solution to detecting traffic signs on road by first classifying the traffic sign images us-ing Convolutional Neural Network (CNN) on the German Traffic Sign Recognition Benchmark (GTSRB)[1] and then detecting the images of Indian Traffic Signs using the Indian Dataset which will be used as testing dataset while building classification model. Therefore this system helps electric cars or self driving cars to recognise the traffic signs efficiently and correctly. The system involves two parts, detection of traffic signs from the environment and classification based on CNN thereby recognising the traffic sign. The classification involves building a CNN model of different filters of dimensions 3 × 3, 5 × 5, 9 × 9, 13 × 13, 15 × 15,19 × 19, 23 × 23, 25 × 25 and 31 ×31 from which the most efficient filter is chosen for further classifying the image detected. The detection involves detecting the traffic sign using YOLO v3-v4 and BLOB detection. Transfer Learning is used for using the trained model for detecting Indian traffic sign images.

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