Wanjun Liu
ABSTRACT
Traffic sign recognition has a wide application prospect in the field of automatic driving. External factors such as illumination, Angle and occlusion will affect the recognition effect of small traffic signs. In order to solve these problems, this paper designs a multi-scale fusion convolutional neural network model (SQ-RCNN) based on feature extraction network Faster RCNN. Firstly, the multi-scale Atrous Spatial Pyramid Pooling (SASPP) module is added to the basic feature extraction network. After multi-scale cavity convolution sampling, the amount of information under each feature is not changed. In this way, the loss of resolution can be reduced and the context information of the same image can be captured. Secondly, the combination structure of two convolution layers and one pooling layer in the original VGG16 model was improved, and the concat operation was adopted to enrich the number of features by merging the number of channels, so as to realize the fusion of features at different scales and improve the accuracy of identifying small targets. In addition, a dropout layer is added to prevent overfitting. The experimental results show that: In this paper, a new network structure SQ-RCNN was used to extract features from CCTSDB data set, the mean average accuracy of traffic sign identification reached 86.96%, at the same time, effectively shorten the training time.
- Möhle Luisa, Bascuñana Pablo, Brackhan Mirjam 2021. Development of deep learning models for microglia analyses in brain tissue using DeePathology™ STUDIO[J] Journal of Neuroscience Methods, DOI:https://doi.org/10.1016/J.JNEUMETH.2021.109371Google Scholar
- Guilai Han, Wei Liu, Benguo Yu, Xiaoling Li, Lu Liu, and Haixia Li. 2020. The Detection and Recognition of Pulmonary Nodules Based on U-net and CNN. In 2020 4th International Conference on Computer Science and Artificial Intelligence(CSAI 2020). Association for Computing Machinery, New York, NY, USA, 134–138. DOI:https://doi.org/10.1145/3445815.3445838Google ScholarDigital Library
- Xiaohan Liu, Jian Jia, and Rui Zhang. 2020. Automatic Detection of Epilepsy EEG based on CNN-LSTM Network Combination Model. In 2020 4th International Conference on Computer Science and Artificial Intelligence(CSAI 2020). Association for Computing Machinery, New York, NY, USA, 225–232. DOI:https://doi.org/10.1145/3445815.3445852Google ScholarDigital Library
- Qian Wang, Haifeng Zhang, Na Min, Zinan Yin.2021. Real-time infrared target recognition method based on moving segmentation and lightweight classification network [J]. Optical Technique, DOI: https://doi.org/ 10.13741/j.cnki.11-1879/o4.2021.04.017.Google Scholar
- Girshick, R. (2015) Fast R-CNN. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 1440-1448. DOI:https://doi.org/10.1109/ICCV.2015.169Google ScholarDigital Library
- Anthony Adole, Eran Edirisinghe, Baihua Li, and Chris Bearchell. 2020. Investigation of Faster-RCNN Inception Resnet V2 on Offline Kanji Handwriting Characters. In Proceedings of the 2020 International Conference on Pattern Recognition and Intelligent Systems(PRIS 2020). Association for Computing Machinery, New York, NY, USA, Article 18, 1–5. DOI:https://doi.org/10.1145/3415048.3416104Google ScholarDigital Library
- Sigit Adinugroho, Putra Pandu Adikara, Edy Santoso, Restu Amara, Kresentia Septiana, and Kenza Dwi Anggita. 2020. Indonesian food identification and detection in the smart nutrition box using faster-RCNN. In Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology(SIET '20). Association for Computing Machinery, New York, NY, USA, 113–117. DOI:https://doi.org/10.1145/3427423.3427429Google ScholarDigital Library
- Ting He, Ying Liu, Yabin Yu 2019 . Application of deep convolutional neural network on feature extraction and detection of wood defects[J] Measurement, DOI:https://doi.org/10.1016/j.measurement.2019.107357Google Scholar
- Zhou Feiyan, Jin Linpeng, 2017. Dong Jun.A review of convolutional neural networks[J].Journal of Computer Science, DOI: https://doi.org/10.11897/SP.J.1016.2017.01229Google Scholar
- Christian Eggert, Dan Zecha, Stephan Brehm, and Rainer Lienhart. 2017. Improving Small Object Proposals for Company Logo Detection. In Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval(ICMR '17). Association for Computing Machinery, New York, NY, USA, 167–174. DOI:https://doi.org/10.1145/3078971.3078990Google ScholarDigital Library
- Liu Jiamin 2017. Detection and diagnosis of colitis on computed tomography using deep convolutional neural networks.[J]. Medical physics, https://doi.org/10.1002/mp.12399Google Scholar
- Bing Xu, Xiaopei He, Zhijian Qu* .2021.Department of Computer Science and Technology, Shandong University of Technology, Zibo, China.DOI: https : // doi.org/10.4236/jcc.2021.93002 Google Scholar
- Zhang Jianhua, Kong Fantao, Wu Jianzhai,et al.2018. Cotton disease recognition model based on improved VGG convolutional neural network. Journal of China Agricultural University, 23(11): 161-171, DOI: https://doi.org/10.11841/j.issn.1007-4333.2018.11.17Google Scholar
- Benhe Gao, Zhongjun Jiang, and Jiaman zhang. 2019. Traffic Sign Detection based on SSD. In Proceedings of the 2019 4th International Conference on Automation, Control and Robotics Engineering(CACRE2019). Association for Computing Machinery, New York, NY, USA, Article 16, 1–6. DOI:https://doi.org/10.1145/3351917.3351988Google ScholarDigital Library
- Jihie Kim, Kwangyong Lim, YoungJung Uh, SeungGyu Kim, Yeongwoo Choi, and Hyeran Byun. 2013. Real-time Korean traffic sign detection and recognition. In Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication(ICUIMC '13). Association for Computing Machinery, New York, NY, USA, Article 19, 1–5. DOI:https://doi.org/10.1145/2448556.2448575Google ScholarDigital Library
- Sheldon Waite, Erdal Oruklu .2013. Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, USA.DOI: https://doi.org/10.4236/jtts.2013.31001Google Scholar
- Li Xudong, Zhang Jianming, Xie Zhipeng,et al. 2020. Fast Recognition Algorithm of Traffic Signs Based on Three-scale Nested Residual Structure [J].Computer Research and Development, https://doi.org/ 10.7544/issn1000-1239.2020.20190445Google Scholar
- Ren Kun, Huang Long, Fan Chunqi,et al.. 2020. Real-time small traffic signs detection algorithm based on multi-scale pixel feature fusion. Signal Processing, https://doi.org/ 10.16798/j.issn.1003-0530.2020.09.010Google Scholar
- Chen Changchuan,Wang Haining,Zhao Yue,et al. 2021. A new algorithm for traffic sign recognition based on deep learning. Telecommunications Technology, https://doi.org/ 10.3969/j.issn.1001-893x.2021.01.012Google Scholar
Index Terms
- Research on Traffic Sign Recognition based on Convolutional Neural Network
Recommendations
Research on Traffic Sign Detection Based on Convolutional Neural Network
VINCI '19: Proceedings of the 12th International Symposium on Visual Information Communication and InteractionTSD (Traffic Sign Detection) is a hotspot in autonomous driving and assisted driving research. TSD research is of great significance for improving road traffic safety. In recent years, CNN (Convolutional Neural Networks) have achieved great success in ...
Traffic Sign Recognition Based on Deep Learning Technique
ICISDM '22: Proceedings of the 6th International Conference on Information System and Data MiningTraffic sign recognition plays a significant role in intelligent transportation system. Therefore, in this paper, I propose a traffic sign recognition algorithm based on Convolutional Neural Network (CNN). The dataset collected to train and test in ...
Traffic Sign Recognition in Harsh Environment Using Attention Based Convolutional Pooling Neural Network
AbstractConvolutional neural networks (CNNs) have achieved significant progress in computer vision systems, helping to efficiently obtain feature information by sliding filters on the input images. However, CNNs have difficulty capturing specific ...
Comments