{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T12:23:21Z","timestamp":1755692601399,"version":"3.40.5"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s11554-024-01554-1","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T06:02:25Z","timestamp":1727416945000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Learning multi-layer interactive residual feature fusion network for real-time traffic sign detection with stage routing attention"],"prefix":"10.1007","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4278-0805","authenticated-orcid":false,"given":"Jianming","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0053-7748","authenticated-orcid":false,"given":"Yao","family":"Yi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4532-8375","authenticated-orcid":false,"given":"Zulou","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8972-5953","authenticated-orcid":false,"given":"Fayez","family":"Alqahtani","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5473-8738","authenticated-orcid":false,"given":"Jin","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,27]]},"reference":[{"issue":"6","key":"1554_CR1","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1007\/s11554-022-01252-w","volume":"19","author":"J Zhang","year":"2022","unstructured":"Zhang, J., Ye, Z., Jin, X., Wang, J., Zhang, J.: Real-time traffic sign detection based on multiscale attention and spatial information aggregator. J. Real-Time Image Process. 19(6), 1155\u20131167 (2022)","journal-title":"J. Real-Time Image Process."},{"key":"1554_CR2","doi-asserted-by":"publisher","first-page":"1420","DOI":"10.1109\/ACCESS.2020.3047091","volume":"9","author":"W Li","year":"2021","unstructured":"Li, W., Qu, Z., Song, H., Wang, P., Xue, B.: The traffic scene understanding and prediction based on image captioning. IEEE Access 9, 1420\u20131427 (2021)","journal-title":"IEEE Access"},{"key":"1554_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2024.109075","volume":"114","author":"J Zhang","year":"2024","unstructured":"Zhang, J., He, Y., Chen, W., Kuang, L.-D., Zheng, B.: Corrformer: context-aware tracking with cross-correlation and transformer. Comput. Elect. Eng. 114, 109075 (2024)","journal-title":"Comput. Elect. Eng."},{"key":"1554_CR4","first-page":"9811","volume":"2021","author":"D Liu","year":"2021","unstructured":"Liu, D., Cui, Y., Tan, W., Chen, Y.: Sg-net: spatial granularity network for one-stage video instance segmentation. IEEE\/CVF Conf. Comput. Vis. Pattern Recogn. (CVPR) 2021, 9811\u20139820 (2021)","journal-title":"IEEE\/CVF Conf. Comput. Vis. Pattern Recogn. (CVPR)"},{"key":"1554_CR5","first-page":"8118","volume":"2021","author":"Y Cui","year":"2021","unstructured":"Cui, Y., Yan, L., Cao, Z., Liu, D.: Tf-blender: temporal feature blender for video object detection. IEEE\/CVF Int. Conf. Comput. Vis. (ICCV) 2021, 8118\u20138127 (2021)","journal-title":"IEEE\/CVF Int. Conf. Comput. Vis. (ICCV)"},{"key":"1554_CR6","doi-asserted-by":"crossref","unstructured":"Liu, D., Cui, Y., Yan, L., Mousas, C., Yang, B., Chen, Y.: Densernet: Weakly supervised visual localization using multi-scale feature aggregation. In: Proceedings of the AAAI Conference on Artificial Intelligence 6101\u20136109 (2021)","DOI":"10.1609\/aaai.v35i7.16760"},{"key":"1554_CR7","unstructured":"Wang, W., Han, C., Zhou, T., Liu, D.: Visual recognition with deep nearest centroids. In: International Conference on Learning Representations (ICLR) (2023)"},{"key":"1554_CR8","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"1554_CR9","doi-asserted-by":"crossref","unstructured":"Zhu, L., Wang, X., Ke, Z., Zhang, W., Lau, R.\u00a0W.: Biformer: vision transformer with bi-level routing attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10323\u201310333 (2023)","DOI":"10.1109\/CVPR52729.2023.00995"},{"key":"1554_CR10","doi-asserted-by":"crossref","unstructured":"Houben, S., Stallkamp, J., Salmen, J., Schlipsing, M., Igel, C.: Detection of traffic signs in real-world images: the German traffic sign detection benchmark. In: The 2013 international joint conference on neural networks (IJCNN), IEEE, pp. 1\u20138 (2013)","DOI":"10.1109\/IJCNN.2013.6706807"},{"key":"1554_CR11","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., Hu, S.: Traffic-sign detection and classification in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2110\u20132118 (2016)","DOI":"10.1109\/CVPR.2016.232"},{"key":"1554_CR12","first-page":"23","volume":"12","author":"J Zhang","year":"2022","unstructured":"Zhang, J., Zou, X., Kuang, L.-D., Wang, J., Sherratt, R.S., Yu, X.: Cctsdb 2021: a more comprehensive traffic sign detection benchmark. Hum. Cent. Comput. Inform. Sci. 12, 23 (2022)","journal-title":"Hum. Cent. Comput. Inform. Sci."},{"key":"1554_CR13","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, Y., Dayoub, F., Sunderhauf, N.: Varifocalnet: an iou-aware dense object detector. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 8514\u20138523 (2021)","DOI":"10.1109\/CVPR46437.2021.00841"},{"key":"1554_CR14","doi-asserted-by":"crossref","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: Centernet: Keypoint triplets for object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 6569\u20136578 (2019)","DOI":"10.1109\/ICCV.2019.00667"},{"key":"1554_CR15","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 9627\u20139636 (2019)","DOI":"10.1109\/ICCV.2019.00972"},{"key":"1554_CR16","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proceedings of the European conference on computer vision, Springer, pp. 213\u2013229 2020","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"1554_CR17","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., Le, Q.\u00a0V.: Efficientdet: scalable and efficient object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 10781\u201310790 (2020)","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"1554_CR18","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"1554_CR19","unstructured":"Liu, S., Huang, D., Wang, Y.: Learning spatial fusion for single-shot object detection (2019). arXiv preprint arXiv:1911.09516"},{"issue":"9","key":"1554_CR20","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904\u20131916 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1554_CR21","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8759\u20138768 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"1554_CR22","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1554_CR23","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.\u00a0S.: Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1554_CR24","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 13713\u201313722 (2021)","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"1554_CR25","unstructured":"Dai, J., Li, Y., He, K., Sun, J.: R-fcn: Object detection via region-based fully convolutional networks. Adv. Neural Inform. Process. Syst. 29 (2016)"},{"key":"1554_CR26","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. Adv. Neural Inform. Process. Syst. 28 (2015)"},{"key":"1554_CR27","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade r-cnn: delving into high quality object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6154\u20136162 (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"1554_CR28","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7263\u20137271 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"1554_CR29","unstructured":"Redmon, J., Farhadi, A.: Yolov3: an incremental improvement (2018). arXiv preprint arXiv:1804.02767"},{"key":"1554_CR30","unstructured":"Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.\u00a0M.: Yolov4: Optimal speed and accuracy of object detection (2020). arXiv preprint arXiv:2004.10934"},{"key":"1554_CR31","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: Yolox: exceeding yolo series in 2021 (2021). arXiv preprint arXiv:2107.08430"},{"issue":"2","key":"1554_CR32","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1109\/TETCI.2024.3349464","volume":"8","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Lv, Y., Tao, J., Huang, F., Zhang, J.: A robust real-time anchor-free traffic sign detector with one-level feature. IEEE Trans. Emerg. Top. Comput. Intell. 8(2), 1437\u20131451 (2024)","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"issue":"4","key":"1554_CR33","doi-asserted-by":"publisher","first-page":"317","DOI":"10.3233\/AIS-220038","volume":"14","author":"J Zhang","year":"2022","unstructured":"Zhang, J., Zheng, Z., Xie, X., Gui, Y., Kim, G.-J.: Reyolo: a traffic sign detector based on network reparameterization and features adaptive weighting. J. Ambient Intell. Smart Environ. 14(4), 317\u2013334 (2022)","journal-title":"J. Ambient Intell. Smart Environ."},{"key":"1554_CR34","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"1554_CR35","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., Lin, D.: Libra r-cnn: towards balanced learning for object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 821\u2013830 (2019)","DOI":"10.1109\/CVPR.2019.00091"},{"key":"1554_CR36","doi-asserted-by":"crossref","unstructured":"Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., Yan, S.: Perceptual generative adversarial networks for small object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1222\u20131230 (2017)","DOI":"10.1109\/CVPR.2017.211"},{"key":"1554_CR37","doi-asserted-by":"publisher","first-page":"57120","DOI":"10.1109\/ACCESS.2019.2913882","volume":"7","author":"Z Liu","year":"2019","unstructured":"Liu, Z., Du, J., Tian, F., Wen, J.: Mr-cnn: a multi-scale region-based convolutional neural network for small traffic sign recognition. IEEE Access 7, 57120\u201357128 (2019)","journal-title":"IEEE Access"},{"key":"1554_CR38","doi-asserted-by":"publisher","first-page":"64145","DOI":"10.1109\/ACCESS.2020.2984554","volume":"8","author":"H Zhang","year":"2020","unstructured":"Zhang, H., Qin, L., Li, J., Guo, Y., Zhou, Y., Zhang, J., Xu, Z.: Real-time detection method for small traffic signs based on yolov3. IEEE Access 8, 64145\u201364156 (2020)","journal-title":"IEEE Access"},{"key":"1554_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10489-019-01511-7","volume":"50","author":"Z Liu","year":"2020","unstructured":"Liu, Z., Li, D., Ge, S.S., Tian, F.: Small traffic sign detection from large image. Appl. Intell. 50, 1\u201313 (2020)","journal-title":"Appl. Intell."}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01554-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01554-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01554-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T15:25:35Z","timestamp":1729005935000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01554-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,27]]},"references-count":39,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["1554"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01554-1","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"type":"print","value":"1861-8200"},{"type":"electronic","value":"1861-8219"}],"subject":[],"published":{"date-parts":[[2024,9,27]]},"assertion":[{"value":"7 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Confict of interest"}}],"article-number":"176"}}