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.

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 Yolo V4 for advanced Traffic Sign Recognition with synthetic training data generated by various GAN

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Christine Dewi ◽  
Rung-Ching Chen ◽  
Yan-Ting Liu ◽  
Xiaoyi Jiang ◽  
Kristoko Dwi Hartomo
Keyword(s):  
Training Data ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Synthetic Training Data

Traffic Sign Recognition Based on ResNet-20 and Deep Mutual Learning

2020 ◽  
Author(s):  
Tianjiao Huo ◽  
Jiaqi Fan ◽  
Xin Li ◽  
Hong Chen ◽  
Bingzhao Gao ◽  
...  
Keyword(s):  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Mutual Learning ◽  
Sign Recognition
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