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Improved VGG Model for Road traffic sign Recognition

Computers, Materials & Continua 2018年第10期57卷 11-24页

作者： Shuren Zhou Wenlong Liang Junguo Li Jeong-Uk Kim Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science&TechnologyChangsha410114China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Department of Energy Grid Sangmyung UniversitySeoul110743Korea

Road traffic sign recognition is an important task in intelligent transportation *** neural networks(CNNs)have achieved a breakthrough in computer vision tasks and made great success in traffic sign *** this paper,it presents a road traffic sign recognition algorithm based on a convolutional neural *** natural scenes,traffic signs are disturbed by factors such as illumination,occlusion,missing and deformation,and the accuracy of recognition decreases,this paper proposes a model called Improved VGG(IVGG)inspired by VGG *** IVGG model includes 9 layers,compared with the original VGG model,it is added max-pooling operation and dropout operation after multiple convolutional layers,to catch the main features and save the training *** paper proposes the method which adds dropout and Batch Normalization(BN)operations after each fully-connected layer,to further accelerate the model convergence,and then it can get better classification *** uses the German traffic sign Recognition Benchmark(GTSRB)dataset in the *** IVGG model enhances the recognition rate of traffic signs and robustness by using the data augmentation and transfer learning,and the spent time is also reduced greatly.

关键词： Intelligent transportation traffic sign deep learning GTSRB data augmentation

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Pre-Locator Incorporating Swin-Transformer Refined Classifier for traffic sign Recognition

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Intelligent Automation & Soft Computing 2023年第8期37卷 2227-2246页

作者： Qiang Luo Wenbin Zheng School of Software Engineering Chengdu University of Information TechnologyChengdu610225China V.C.&V.R.Key Lab of Sichuan Province Sichuan Normal UniversityChengdu610068China

In the field of traffic sign recognition,traffic signs usually occupy very small areas in the input *** object detection algorithms directly reduce the original image to a specific size for the input model during the detection process,which leads to the loss of small object ***-tionally,classification tasks are more sensitive to information loss than local-ization *** paper proposes a novel traffic sign recognition approach,in which a lightweight pre-locator network and a refined classification network are *** pre-locator network locates the sub-regions of the traffic signs from the original image,and the refined classification network performs the refinement recognition task in the ***,an innovative module(named SPP-ST)is proposed,which combines the Spatial Pyramid Pool module(SPP)and the Swin-Transformer module as a new feature extractor to learn the special spatial information of traffic sign *** results show that the proposed method is superior to the state-of-the-art methods(82.1 mAP achieved on 218 categories in the TT100k dataset,an improvement of 19.7 percentage points compared to the previous method).Moreover,both the result analysis and the output visualizations further demonstrate the effectiveness of our proposed *** source code and datasets of this work are available at https://***/DijiesitelaQ/TSOD.

关键词： traffic sign recognition swin-transformer YOLOX small object

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Research and Implementation of traffic sign Recognition Algorithm Model Based on Machine Learning

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Journal of Software Engineering and Applications 2023年第6期16卷 193-210页

作者： Yuanzhou Wei Meiyan Gao Jun Xiao Chixu Liu Yuanhao Tian Ya He College of Engineering and Computing Florida International University Miami USA College of Intelligent Equipment Shandong University of Science and Technology Qingdao China Steven J. Green School of International & Public Affairs Florida International University Miami USA School of Economics Capital University of Economics and Business Beijing China

traffic sign recognition is an important task in intelligent transportation systems, which can improve road safety and reduce accidents. Algorithms based on deep learning have achieved remarkable results in traffic sign recognition in recent years. In this paper, we build traffic sign recognition algorithms based on ResNet and CNN models, respectively. We evaluate the proposed algorithm on public datasets and compare. We first use the dataset of traffic sign images from Kaggle. And then designed ResNet-based and CNN-based architectures that can effectively capture the complex features of traffic signs. Our experiments show that our ResNet-based model achieves a recognition accuracy of 99% on the test set, and our CNN-based model achieves a recognition accuracy of 98% on the test set. Our proposed approach has the potential to improve traffic safety and can be used in various intelligent transportation systems.

关键词： CNN traffic sign ResNet Recognition Neural Network TensorFlow

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A Corpus-based Study of English Translation of Chinese traffic signs

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海外英语 2020年第2期 56-58,74页

作者：温梦媛武汉理工大学

This paper takes Wuhan’s traffic signs as the research object,and collects the Chinese and English traffic signs in large quantities to build a small *** to the text type theory proposed by German functionalist school Katarina Rice,in this article the traffic signs are classified into three categories:information type text identifier,expression type text identifier and opera⁃tion type text identifier according to six functions including indication,prompt,restriction,compulsory,persuasion and *** attempts to reveal the characteristics of Chinese signs and English translations of different text types,and to explore the translation and semantic rhyme of the word"forbidden"in the English translation of high-frequency vocabulary in traffic *** aims to pro⁃vide reference for the English translation of traffic signs,create a good language environment,shape a good city image,and increase the degree of China's internationalization.

关键词： corpus traffic sign text type theory semantic prosody

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