{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T16:07:58Z","timestamp":1774454878764,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T00:00:00Z","timestamp":1611100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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).<\/jats:p>","DOI":"10.3390\/s21030686","type":"journal-article","created":{"date-parts":[[2021,1,21]],"date-time":"2021-01-21T00:53:41Z","timestamp":1611190421000},"page":"686","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Learning Region-Based Attention Network for Traffic Sign Recognition"],"prefix":"10.3390","volume":"21","author":[{"given":"Ke","family":"Zhou","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center of Steel Technology, University of Science and Technology, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufei","family":"Zhan","sequence":"additional","affiliation":[{"name":"School of Advanced Engineering, University of Science and Technology, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongmei","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Stallkamp, J., Schlipsing, M., Salmen, J., and Igel, C. (August, January 31). The German traffic sign recognition benchmark: A multi-class classification competition. Proceedings of the 2011 International Joint Conference on Neural Networks, San Jose, CA, USA.","DOI":"10.1109\/IJCNN.2011.6033395"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.neunet.2012.02.016","article-title":"Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition","volume":"32","author":"Stallkamp","year":"2012","journal-title":"Neural Netw."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mathias, M., Timofte, R., Benenson, R., and Van Gool, L. (2013, January 4\u20139). Traffic Sign Recognition\u2014How far are we from the solution?. Proceedings of the International Joint Conference on Neural Networks (IJCNN 2013), Dallas, TX, USA.","DOI":"10.1109\/IJCNN.2013.6707049"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Larsson, F., and Felsberg, M. (2011, January 23\u201325). Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition. Proceedings of the 17th Scandinavian Conference on Image Analysis, Ystad, Sweden.","DOI":"10.1007\/978-3-642-21227-7_23"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., and Hu, S. (2016, January 27\u201330). Traffic-sign detection and classification in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.232"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Han, Y., Virupakshappa, K., and Oruklu, E. (2015, January 21\u201323). Robust traffic sign recognition with feature extraction and k-NN classification methods. Proceedings of the 2015 IEEE International Conference on Electro\/Information Technology (EIT), Dekalb, IL, USA.","DOI":"10.1109\/EIT.2015.7293386"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zaklouta, F., Stanciulescu, B., and Hamdoun, O. (August, January 31). Traffic sign classification using K-d trees and Random Forests. Proceedings of the 2011 International Joint Conference on Neural Networks, San Jose, CA, USA.","DOI":"10.1109\/IJCNN.2011.6033494"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1109\/TITS.2007.895311","article-title":"Road-Sign Detection and Recognition Based on Support Vector Machines","volume":"8","year":"2007","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fleyeh, H., and Dougherty, M. (2008, January 4\u20136). Traffic sign classification using invariant features and Support Vector Machines. Proceedings of the 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands.","DOI":"10.1109\/IVS.2008.4621132"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ciresan, D., Meier, U., Masci, J., and Schmidhuber, J. (August, January 31). A committee of neural networks for traffic sign classification. Proceedings of the 2011 International Joint Conference on Neural Networks, San Jose, CA, USA.","DOI":"10.1109\/IJCNN.2011.6033458"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sermanet, P., and LeCun, Y. (August, January 31). Traffic sign recognition with multi-scale Convolutional Networks. Proceedings of the 2011 International Joint Conference on Neural Networks, San Jose, CA, USA.","DOI":"10.1109\/IJCNN.2011.6033589"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ciresan, D., Meier, U., and Schmidhuber, J. (2012, January 16\u201321). Multi-column deep neural networks for image classification. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"59803","DOI":"10.1109\/ACCESS.2018.2873948","article-title":"MicronNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-Time Embedded Traffic Sign Classification","volume":"6","author":"Wong","year":"2018","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1109\/TITS.2018.2843815","article-title":"Real-Time Traffic Sign Recognition Based on Efficient CNNs in the Wild","volume":"20","author":"Li","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pavlov, A.L., Karpyshev, P.A., Ovchinnikov, G.V., Oseledets, I.V., and Tsetserukou, D. (2019, January 20\u201324). IceVisionSet: Lossless video dataset collected on Russian winter roads with traffic sign annotations. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8794341"},{"key":"ref_16","unstructured":"Sun, K., Zhao, Y., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W., and Wang, J. (2019). High-resolution representations for labeling pixels and regions. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., and Lin, D. (2019, January 15\u201321). Libra r-cnn: Towards balanced learning for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00091"},{"key":"ref_18","unstructured":"Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., Andriluka, M., Rajpurkar, P., Migimatsu, T., and Cheng-Yue, R. (2015). An empirical evaluation of deep learning on highway driving. arXiv."},{"key":"ref_19","first-page":"255","article-title":"A cognitively motivated method for classification of occluded traffic signs","volume":"47","author":"Hou","year":"2016","journal-title":"IEEE Trans. Syst. ManCybern. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Khan, J.A., Yeo, D., and Shin, H. (2018). New dark area sensitive tone mapping for deep learning based traffic sign recognition. Sensors, 18.","DOI":"10.3390\/s18113776"},{"key":"ref_21","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv."},{"key":"ref_22","unstructured":"Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R.S., and Bengio, Y. (2015, January 6\u201311). Show, attend and tell: Neural image caption generation with visual attention. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Luong, M.-T., Pham, H., and Manning, C.D. (2015). Effective approaches to attention-based neural machine translation. arXiv.","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref_24","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.neunet.2018.01.005","article-title":"Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods","volume":"99","year":"2018","journal-title":"Neural Netw."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Uittenbogaard, R., Sebastian, C., Viiverberg, J., Boom, B., and De With, P.H. (2018, January 20\u201324). Conditional Transfer with Dense Residual Attention: Synthesizing traffic signs from street-view imagery. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8545149"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, J., Hui, L., Lu, J., and Zhu, Y. (2018, January 20\u201324). Attention-based Neural Network for Traffic Sign Detection. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8546289"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2551","DOI":"10.1007\/s11063-020-10211-0","article-title":"Traffic Sign Recognition in Harsh Environment Using Attention Based Convolutional Pooling Neural Network","volume":"51","author":"Chung","year":"2020","journal-title":"Neural Process. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Qiu, K., Fu, J., and Fu, D. (2019, January 8\u201312). Learning Recurrent Structure-Guided Attention Network for Multi-person Pose Estimation. Proceedings of the 2019 IEEE International Conference on Multimedia and Expo (ICME), Shanghai, China.","DOI":"10.1109\/ICME.2019.00079"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/686\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:13:00Z","timestamp":1760159580000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/686"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,20]]},"references-count":32,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21030686"],"URL":"https:\/\/doi.org\/10.3390\/s21030686","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,20]]}}}