{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T16:23:16Z","timestamp":1782404596615,"version":"3.54.5"},"reference-count":56,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,6,3]],"date-time":"2023-06-03T00:00:00Z","timestamp":1685750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"excellent young and middle-aged scientific and technological innovation teams in Colleges and universities of Hubei Province","award":["T2021009"],"award-info":[{"award-number":["T2021009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The detection of traffic signs is easily affected by changes in the weather, partial occlusion, and light intensity, which increases the number of potential safety hazards in practical applications of autonomous driving. To address this issue, a new traffic sign dataset, namely the enhanced Tsinghua-Tencent 100K (TT100K) dataset, was constructed, which includes the number of difficult samples generated using various data augmentation strategies such as fog, snow, noise, occlusion, and blur. Meanwhile, a small traffic sign detection network for complex environments based on the framework of YOLOv5 (STC-YOLO) was constructed to be suitable for complex scenes. In this network, the down-sampling multiple was adjusted, and a small object detection layer was adopted to obtain and transmit richer and more discriminative small object features. Then, a feature extraction module combining a convolutional neural network (CNN) and multi-head attention was designed to break the limitations of ordinary convolution extraction to obtain a larger receptive field. Finally, the normalized Gaussian Wasserstein distance (NWD) metric was introduced to make up for the sensitivity of the intersection over union (IoU) loss to the location deviation of tiny objects in the regression loss function. A more accurate size of the anchor boxes for small objects was achieved using the K-means++ clustering algorithm. Experiments on 45 types of sign detection results on the enhanced TT100K dataset showed that the STC-YOLO algorithm outperformed YOLOv5 by 9.3% in the mean average precision (mAP), and the performance of STC-YOLO was comparable with that of the state-of-the-art methods on the public TT100K dataset and CSUST Chinese Traffic Sign Detection Benchmark (CCTSDB2021) dataset.<\/jats:p>","DOI":"10.3390\/s23115307","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T02:18:29Z","timestamp":1685931509000},"page":"5307","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":81,"title":["STC-YOLO: Small Object Detection Network for Traffic Signs in Complex Environments"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2060-8991","authenticated-orcid":false,"given":"Huaqing","family":"Lai","sequence":"first","affiliation":[{"name":"School of Electric and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangyan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electric and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihua","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electric and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zi","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Electric and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Electric and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4113","DOI":"10.1007\/s10489-018-1199-x","article-title":"Fast and robust road sign detection in driver assistance systems","volume":"48","author":"Zhang","year":"2018","journal-title":"Appl. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/s10489-013-0425-9","article-title":"Boosting-SVM: Effective learning with reduced data dimension","volume":"39","author":"Wang","year":"2013","journal-title":"Appl. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s11554-013-0348-z","article-title":"Efficient algorithm for automatic road sign recognition and its hardware implementation","volume":"9","author":"Souani","year":"2014","journal-title":"J. Real-Time Image Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1007\/s10489-018-1298-8","article-title":"Traffic sign detection based on visual co-saliency in complex scenes","volume":"49","author":"Yu","year":"2019","journal-title":"Appl. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1109\/TITS.2012.2208909","article-title":"Real-time detection and recognition of road traffic signs","volume":"13","author":"Greenhalgh","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.eswa.2015.11.018","article-title":"On circular traffic sign detection and recognition","volume":"48","author":"Berkaya","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_7","first-page":"1137","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201322). Cascade r-cnn: Delving into high quality object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_10","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_11","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A single-stage object detection framework for industrial applications. arXiv."},{"key":"ref_12","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part I 14.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_14","unstructured":"Fu, C.Y., Liu, W., Ranga, A., Tyagi, A., and Berg, A.C. (2017). Dssd: Deconvolutional single shot detector. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"64145","DOI":"10.1109\/ACCESS.2020.2984554","article-title":"Real-time detection method for small traffic signs based on Yolov3","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","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_17","first-page":"4285436","article-title":"Traffic sign detection based on SSD combined with receptive field module and path aggregation network","volume":"2022","author":"Wu","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201322). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Houben, S., Stallkamp, J., Salmen, J., Schlipsing, M., and Igel, C. (2013, January 4\u20139). Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark. In Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA.","DOI":"10.1109\/IJCNN.2013.6706807"},{"key":"ref_20","unstructured":"Zhang, J., Zou, X., Kuang, L.D., Wang, J., Sherratt, R.S., and Yu, X. (2022). Human-Centric Computing and Information Sciences, Springer."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yan, B., Li, J., Yang, Z., Zhang, X., and Hao, X. (2022). AIE-YOLO: Auxiliary Information Enhanced YOLO for Small Object Detection. Sensors, 22.","DOI":"10.3390\/s22218221"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hnewa, M., and Radha, H. (2021, January 19\u201322). Multiscale domain adaptive yolo for cross-domain object detection. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506039"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1177\/0954407020950054","article-title":"Multi-scale traffic sign detection model with attention","volume":"235","author":"Fan","year":"2021","journal-title":"Proc. Inst. Mech. Eng. Part D J. Automob. Eng."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Zhaosheng, Y., Tao, L., Tianle, Y., Chengxin, J., and Chengming, S. (2022). Rapid Detection of Wheat Ears in Orthophotos From Unmanned Aerial Vehicles in Fields Based on YOLOX. Front. Plant Sci., 1272.","DOI":"10.3389\/fpls.2022.851245"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bai, Y., Zhang, Y., Ding, M., and Ghanem, B. (2018, January 8\u201314). Sod-mtgan: Small object detection via multi-task generative adversarial network. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_13"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bai, Y., Zhang, Y., Ding, M., and Ghanem, B. (2018, January 18\u201322). Finding tiny faces in the wild with generative adversarial network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00010"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, S., and Huang, D. (2018, January 8\u201314). Receptive field block net for accurate and fast object detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yu, F., Koltun, V., and Funkhouser, T. (2017, January 21\u201326). Dilated residual networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.75"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1109\/TITS.2010.2054084","article-title":"Goal evaluation of segmentation algorithms for traffic sign recognition","volume":"11","year":"2010","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1016\/j.patcog.2014.05.017","article-title":"Traffic sign detection via interest region extraction","volume":"48","author":"Salti","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1109\/TVT.2003.810999","article-title":"Road-sign detection and tracking","volume":"52","author":"Fang","year":"2003","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1109\/TITS.2008.922935","article-title":"Real-time speed sign detection using the radial symmetry detector","volume":"9","author":"Barnes","year":"2008","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3116","DOI":"10.1109\/TITS.2015.2433019","article-title":"Detection of US traffic signs","volume":"16","author":"Liu","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"29742","DOI":"10.1109\/ACCESS.2020.2972338","article-title":"A cascaded R-CNN with multiscale attention and imbalanced samples for traffic sign detection","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"33593","DOI":"10.1007\/s11042-021-11413-x","article-title":"Traffic sign detection algorithm based on feature expression enhancement","volume":"80","author":"Sun","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, Y., Shi, G., Li, Y., and Zhao, Z. (2022). M-YOLO: Traffic sign detection algorithm applicable to complex scenarios. Symmetry, 14.","DOI":"10.3390\/sym14050952"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7\u201312). Distance-IoU loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","article-title":"Focal and efficient IOU loss for accurate bounding box regression","volume":"506","author":"Zhang","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_42","unstructured":"Gevorgyan, Z. (2022). SIoU loss: More powerful learning for bounding box regression. arXiv."},{"key":"ref_43","unstructured":"Wang, J., Xu, C., Yang, W., and Yu, L. (2021). A normalized Gaussian Wasserstein distance for tiny object detection. arXiv."},{"key":"ref_44","unstructured":"Yu, Z., Huang, H., Chen, W., Su, Y., Liu, Y., and Wang, X. (2022). YOLO-FaceV2: A Scale and Occlusion Aware Face Detector. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C.L., and Dollar, P. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the Computer Vision\u2013ECCV 2014, 13th European Conference, Zurich, Switzerland. Proceedings, Part V 13.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kang, S.H., and Park, J.S. (2023). Aligned Matching: Improving Small Object Detection in SSD. Sensors, 23.","DOI":"10.3390\/s23052589"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2233","DOI":"10.1007\/s00521-021-06526-1","article-title":"A real-time and high-precision method for small traffic-signs recognition","volume":"34","author":"Chen","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Hu, J., Wang, Z., Chang, M., Xie, L., Xu, W., and Chen, N. (2022). PSG-Yolov5: A Paradigm for Traffic Sign Detection and Recognition Algorithm Based on Deep Learning. Symmetry, 14.","DOI":"10.3390\/sym14112262"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1007\/s11554-022-01252-w","article-title":"Real-time traffic sign detection based on multiscale attention and spatial information aggregator","volume":"19","author":"Zhang","year":"2022","journal-title":"J. Real-Time Image Process."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"7982","DOI":"10.1007\/s11227-021-04230-4","article-title":"Traffic sign detection based on improved faster R-CNN for autonomous driving","volume":"78","author":"Li","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_51","unstructured":"Du, D., Zhu, P., Wen, L., Bian, X., Ling, H., Hu, Q., Peng, T., Zheng, J., Wang, X., and Zhang, Y. (2019, January 27\u201328). VisDrone-DET2019: The vision meets drone object detection in image challenge results. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, Seoul, Republic of Korea."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"11204","DOI":"10.1109\/JSTARS.2021.3122152","article-title":"Unsupervised Cluster Guided Object Detection in Aerial Images","volume":"14","author":"Liao","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"8448","DOI":"10.1007\/s10489-021-02893-3","article-title":"RSOD: Real-time small object detection algorithm in UAV-based traffic monitoring","volume":"52","author":"Sun","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Liu, B., Luo, H., Wang, H., and Wang, S. (2022). YOLOv3_ReSAM: A small-target detection method. Electronics, 11.","DOI":"10.3390\/electronics11101635"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., and Zhao, Q. (2021, January 10\u201317). TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s44196-021-00056-3","article-title":"GLE-Net: A global and local ensemble network for aerial object detection","volume":"15","author":"Liao","year":"2022","journal-title":"Int. J. Comput. Intell. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5307\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:48:24Z","timestamp":1760125704000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5307"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,3]]},"references-count":56,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23115307"],"URL":"https:\/\/doi.org\/10.3390\/s23115307","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,3]]}}}