scholarly journals Traffic Sign Classification Using CNN

2021 ◽  
Vol 9 (8) ◽  
pp. 1952-1956
Author(s):  
Kolachalama Venkata Naga Sreya
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
Efficiency Index ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Conventional Model ◽  
Sign Recognition ◽  
Index Terms ◽  
Traffic Sign Classification ◽  
Autonomous Electric Vehicles

Abstract: With the increasing necessity of autonomous electric vehicles as time passes by, there are a lot of technical prospects within the structure that have a lot of scope for advancement. One such prospect is traffic sign recognition. Several models have already been developed and are in practice but it is evident to everyone within this field that there is still a lot of untapped potential. In this project we implement feature classification using convolutional neural networks to achieve an efficiency and accuracy higher than that of a conventional model. The system first converts the image into grayscale and then three layers of the image are created. By using skilled convolutional neural network which incorporates crucial data of traffic signs and images, they are parallelly assigned to corresponding classes. Results have shown that this system works with great efficiency. Index Terms: Traffic sign, GTSRB, ALVINN, Neural Network, CNN, LeNet, ReLU, Evaluation, Epoch

scholarly journals Learning Region-Based Attention Network for Traffic Sign Recognition

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 686
Author(s):  
Ke Zhou ◽  
Yufei Zhan ◽  
Dongmei Fu
Keyword(s):  
High Resolution ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Attention Network ◽  
Sign Recognition ◽  
Learning Region ◽  
Robust Network ◽  
Traffic Sign Classification

Traffic sign recognition in poor environments has always been a challenge in self-driving. Although a few works have achieved good results in the field of traffic sign recognition, there is currently a lack of traffic sign benchmarks containing many complex factors and a robust network. In this paper, we propose an ice environment traffic sign recognition benchmark (ITSRB) and detection benchmark (ITSDB), marked in the COCO2017 format. The benchmarks include 5806 images with 43,290 traffic sign instances with different climate, light, time, and occlusion conditions. Second, we tested the robustness of the Libra-RCNN and HRNetv2p on the ITSDB compared with Faster-RCNN. The Libra-RCNN performed well and proved that our ITSDB dataset did increase the challenge in this task. Third, we propose an attention network based on high-resolution traffic sign classification (PFANet), and conduct ablation research on the design parallel fusion attention module. Experiments show that our representation reached 93.57% accuracy in ITSRB, and performed as well as the newest and most effective networks in the German traffic sign recognition dataset (GTSRB).

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.

Traffic Sign Recognition Based on Improved Deep Convolution Neural Network

2018 ◽  
Vol 55 (12) ◽  
pp. 121009 ◽  
Author(s):  
马永杰 Ma Yongjie ◽  
李雪燕 Li Xueyan ◽  
宋晓凤 Song Xiaofeng
Keyword(s):  
Neural Network ◽  
Convolution Neural Network ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Deep Convolution Neural Network

Few-shot traffic sign recognition with clustering inductive bias and random neural network

2020 ◽  
Vol 100 ◽  
pp. 107160 ◽  
Author(s):  
Shichao Zhou ◽  
Chenwei Deng ◽  
Zhengquan Piao ◽  
Baojun Zhao
Keyword(s):  
Neural Network ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Inductive Bias ◽  
Sign Recognition ◽  
Random Neural Network

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.

Fast Branch Convolutional Neural Network for Traffic Sign Recognition

2017 ◽  
Vol 9 (3) ◽  
pp. 114-126 ◽  
Author(s):  
Wenzheng Hu ◽  
Qing Zhuo ◽  
Changshui Zhang ◽  
Jianke Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition
Sign in / Sign up

Export Citation Format

Share Document