scholarly journals Traffic sign recognition based on HOG feature extraction

2021 ◽  
Author(s):  
Guo Shuqing ◽  
Song Yucong
Keyword(s):  
Feature Extraction ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition

scholarly journals Finely Crafted Features for Traffic Sign Recognition

2022 ◽  
Vol 16 ◽  
pp. 159-170
Author(s):  
Wei Li ◽  
Haiyu Song ◽  
Pengjie Wang
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Image Features ◽  
Training Dataset ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Discriminative Ability ◽  
Traffic Signs ◽  
Feature Extraction Method ◽  
Sign Recognition

Traffic sign recognition (TSR) is the basic technology of the Advanced Driving Assistance System (ADAS) and intelligent automobile, whileas high-qualified feature vector plays a key role in TSR. Therefore, the feature extraction of TSR has become an active research in the fields of computer vision and intelligent automobiles. Although deep learning features have made a breakthrough in image classification, it is difficult to apply to TSR because of its large scale of training dataset and high space-time complexity of model training. Considering visual characteristics of traffic signs and external factors such as weather, light, and blur in real scenes, an efficient method to extract high-qualified image features is proposed. As a result, the lower-dimension feature can accurately depict the visual feature of TSR due to powerful descriptive and discriminative ability. In addition, benefiting from a simple feature extraction method and lower time cost, our method is suitable to recognize traffic signs online in real-world applications scenarios. Extensive quantitative experimental results demonstrate the effectiveness and efficiency of our method.

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

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).

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

A Lightweight Model For Real-time Traffic Sign Recognition

2020 ◽  
Author(s):  
Trung-Hieu Nguyen ◽  
Vu-Hoang Tran ◽  
Van-Dung Do ◽  
Van-Thuyen Ngo ◽  
Thanh-Thanh Ngo-Quang
Keyword(s):  
Real Time ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Real Time Traffic ◽  
Sign Recognition
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