{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T03:40:28Z","timestamp":1776742828926,"version":"3.51.2"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T00:00:00Z","timestamp":1641945600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the key scientific research project of higher school of Henan Province","award":["21A520022"],"award-info":[{"award-number":["21A520022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Aiming at recognizing small proportion, blurred and complex traffic sign in natural scenes, a traffic sign detection method based on RetinaNet-NeXt is proposed. First, to ensure the quality of dataset, the data were cleaned and enhanced to denoise. Secondly, a novel backbone network ResNeXt was employed to improve the detection accuracy and effection of RetinaNet. Finally, transfer learning and group normalization were adopted to accelerate our network training. Experimental results show that the precision, recall and mAP of our method, compared with the original RetinaNet, are improved by 9.08%, 9.09% and 7.32%, respectively. Our method can be effectively applied to traffic sign detection.<\/jats:p>","DOI":"10.3390\/e24010112","type":"journal-article","created":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T04:15:25Z","timestamp":1641960925000},"page":"112","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Visual Recognition of Traffic Signs in Natural Scenes Based on Improved RetinaNet"],"prefix":"10.3390","volume":"24","author":[{"given":"Shangwang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China"},{"name":"Henan Engineering Laboratory of \u2018Smart Business and Internet of Things Technology\u2019, Xinxiang 453007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tongbo","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China"},{"name":"Henan Engineering Laboratory of \u2018Smart Business and Internet of Things Technology\u2019, Xinxiang 453007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiufang","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China"},{"name":"Henan Engineering Laboratory of \u2018Smart Business and Internet of Things Technology\u2019, Xinxiang 453007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China"},{"name":"Henan Engineering Laboratory of \u2018Smart Business and Internet of Things Technology\u2019, Xinxiang 453007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changgeng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China"},{"name":"Henan Engineering Laboratory of \u2018Smart Business and Internet of Things Technology\u2019, Xinxiang 453007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4316","DOI":"10.1109\/TITS.2020.3032227","article-title":"Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions","volume":"22","author":"Muhammad","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","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":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016). SSD: Single Shot MultiBox Detector. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., and Hu, S. (July, January 26). Traffic-Sign Detection and Classification in the Wild. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.232"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9984787","DOI":"10.1155\/2021\/9984787","article-title":"A Novel Neural Network Model for Traffic Sign Detection and Recognition under Extreme Conditions","volume":"2021","author":"Wan","year":"2021","journal-title":"J. Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Song, G. (2021, January 27\u201328). An Improved Traffic Sign Recognition Algorithm Based on Deep Learning. Proceedings of the 2021 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), Xi\u2019an, China.","DOI":"10.1109\/ICITBS53129.2021.00009"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lodhi, A., Singhal, S., and Massoudi, M. (2021, January 20\u201322). Car Traffic Sign Recognizer Using Convolutional Neural Network CNN. Proceedings of the 2021 6th International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India.","DOI":"10.1109\/ICICT50816.2021.9358594"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102700","DOI":"10.1016\/j.scs.2020.102700","article-title":"Cascade Saccade Machine Learning Network with Hierarchical Classes for Traffic Sign Detection","volume":"67","author":"Liu","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1109\/TITS.2019.2913588","article-title":"Deep Learning for Large-Scale Traffic-Sign Detection and Recognition","volume":"21","author":"Tabernik","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, Z., Song, R., Yan, C., and Qi, Y. (2021). Detection-by-Tracking of Traffic Signs in Videos. Appl. Intell.","DOI":"10.1007\/s10489-021-02838-w"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2021.04.083","article-title":"Group Multi-Scale Attention Pyramid Network for Traffic Sign Detection","volume":"452","author":"Shen","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, E., Rohit, M., Fasfous, N., Frickenstein, A., Mzid, A., Nagaraja, N., Zeisler, J., and Stechele, W. (2021, January 11\u201317). Investigating Binary Neural Networks for Traffic Sign Detection and Recognition. Proceedings of the 2021 IEEE Intelligent Vehicles Symposium (IV), Nagoya, Japan.","DOI":"10.1109\/IV48863.2021.9575557"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhou, K., Zhan, Y., and Fu, D. (2021). Learning Region-Based Attention Network for Traffic Sign Recognition. Sensors, 21.","DOI":"10.3390\/s21030686"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4388","DOI":"10.1109\/TITS.2019.2941081","article-title":"Traffic Signs Detection and Classification for European Urban Environments","volume":"21","author":"Ruichek","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.neucom.2021.03.049","article-title":"TSingNet: Scale-Aware and Context-Rich Feature Learning for Traffic Sign Detection and Recognition in the Wild","volume":"447","author":"Liu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1007\/s10489-020-01801-5","article-title":"Transfer Learning Based Hybrid 2D-3D CNN for Traffic Sign Recognition and Semantic Road Detection Applied in Advanced Driver Assistance Systems","volume":"51","author":"Bayoudh","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_21","first-page":"1251","article-title":"Pseudo Sample Regularization Faster R-CNN for Traffic Sign Detection","volume":"51","author":"Li","year":"2021","journal-title":"J. Jilin Univ. (Eng. Technol. Ed.)"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lopez-Montiel, M., Rubio, Y., S\u00e1nchez-Adame, M., and Orozco-Rosas, U. (2019, January 13\u201314). Evaluation of Algorithms for Traffic Sign Detection. Proceedings of the Optics and Photonics for Information Processing XIII., International Society for Optics and Photonics, San Diego, California, USA.","DOI":"10.1117\/12.2529709"},{"key":"ref_23","first-page":"107","article-title":"Detection Method of Traffic Signs Based on Color Pair and MSER in the Complex Environment","volume":"42","author":"Dai","year":"2018","journal-title":"Beijing Jiaotong Daxue Xuebao\/J. Beijing Jiaotong Univ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wu, X., Wei, Z., Hu, Y., and Wang, L. (2020, January 27\u201329). Traffic Sign Detection Method Using Multi-Color Space Fusion. Proceedings of the 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China.","DOI":"10.1109\/ICAICA50127.2020.9182603"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"38931","DOI":"10.1109\/ACCESS.2020.2975828","article-title":"Multi-Feature Fusion and Enhancement Single Shot Detector for Traffic Sign Recognition","volume":"8","author":"Jin","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","first-page":"2079","article-title":"Image Segmentation Algorithm Based on Context Fuzzy C-Means Clustering","volume":"43","author":"Xu","year":"2021","journal-title":"Dianzi Yu Xinxi Xuebao\/J. Electron. Inform. Technol."},{"key":"ref_27","unstructured":"Prakash, A., Vigneshwaran, D., Ayyalu, R., and Sree, S. (2021, January 8\u201310). Traffic Sign Recognition Using Deeplearning for Autonomous Driverless Vehicles. Proceedings of the 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India."},{"key":"ref_28","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 International Joint Conference on Neural Networks, San Jose, CA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lv, H., Dang, X., Yang, D., and Zhu, Q. (2021, January 15\u201317). Research and Design of Traffic Recognition System Based on Hilens. Proceedings of the Tenth International Symposium on Precision Mechanical Measurements, Qingdao, China.","DOI":"10.1117\/12.2611639"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhu, H., and Zhang, C. (2018, January 15\u201317). Real-Time Traffic Sign Detection Based on YOLOv2. Proceedings of the 2018 International Conference on Image and Video Processing, and Artificial Intelligence, Shanghai, China.","DOI":"10.1117\/12.2513869"},{"key":"ref_31","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. Off. J. Int. Neural Netw. Soc."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, Z., Xiao, Z., and Yan, Z. (2019, January 22\u201324). Traffic Sign Recognition Based on Convolutional Neural Network Model. Proceedings of the 2019 Chinese Automation Congress (CAC), Hangzhou, China.","DOI":"10.1109\/CAC51589.2020.9327830"},{"key":"ref_33","unstructured":"Zhao, Q., Shen, Y., and Zhang, Y. (2019, January 23\u201325). Video-Based Traffic Sign Detection and Recognition. Proceedings of the 2019 International Conference on Image and Video Processing, and Artificial Intelligence, Shanghai, China."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, J., Jia, K., Chen, W., Lv, Z., and Zhang, R. (2021). A Real-Time and High-Precision Method for Small Traffic-Signs Recognition. Neural Comput. Appl., 1\u201313.","DOI":"10.1007\/s00521-021-06526-1"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zha, M., Qian, W., Yi, W., and Hua, J. (2021). A Lightweight YOLOv4-Based Forestry Pest Detection Method Using Coordinate Attention and Feature Fusion. Entropy, 23.","DOI":"10.3390\/e23121587"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yang, L., Luo, J., Song, X., Menglong, L., Wen, P., and Xiong, Z. (2021). Robust Vehicle Speed Measurement Based on Feature Information Fusion for Vehicle Multi-Characteristic Detection. Entropy, 23.","DOI":"10.3390\/e23070910"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1007\/s11263-019-01198-w","article-title":"Group Normalization","volume":"128","author":"Wu","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_39","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv."},{"key":"ref_40","first-page":"040901","article-title":"Development of Convolutional Neural Network and Its Application in Image Classification: A Survey","volume":"58","author":"Wang","year":"2019","journal-title":"Opt. Eng."},{"key":"ref_41","unstructured":"Jocher, G., Stoken, A., Chaurasia, A., Borovec, J., Xie, T., Kwon, Y., Michael, K., Changyu, L., and Fang, J. (2021, December 01). Ultralytics\/Yolov5 V6.0\u2014YOLOv5n \u2018Nano\u2019 Models, Roboflow Integration, TensorFlow Export, OpenCV DNN Support on GitHub. Available online: https:\/\/newreleases.io\/project\/github\/ultralytics\/yolov5\/release\/v6.0."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/1\/112\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:27:18Z","timestamp":1760362038000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/1\/112"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,12]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["e24010112"],"URL":"https:\/\/doi.org\/10.3390\/e24010112","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,12]]}}}