{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T05:59:13Z","timestamp":1777010353928,"version":"3.51.4"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key R","award":["2022ZD0119500"],"award-info":[{"award-number":["2022ZD0119500"]}]},{"name":"D Program of China","award":["2022ZD0119501"],"award-info":[{"award-number":["2022ZD0119501"]}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["U1931207"],"award-info":[{"award-number":["U1931207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Sci. & Tech. Development Fund of Shandong Province of China","award":["ZR2022MF288"],"award-info":[{"award-number":["ZR2022MF288"]}]},{"name":"the Taishan Scholar Program of Shandong Province","award":["ts20190936"],"award-info":[{"award-number":["ts20190936"]}]},{"name":"SDUST Research Fund","award":["2015TDJH102"],"award-info":[{"award-number":["2015TDJH102"]}]},{"name":"Shandong Chongqing Science and technology cooperation project","award":["cstc2020jscx-lyjsAX0008"],"award-info":[{"award-number":["cstc2020jscx-lyjsAX0008"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s11554-024-01497-7","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T10:02:59Z","timestamp":1718791379000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Rtsds:a real-time and efficient method for detecting surface defects in strip steel"],"prefix":"10.1007","volume":"21","author":[{"given":"Qingtian","family":"Zeng","sequence":"first","affiliation":[]},{"given":"Daibai","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Minghao","family":"Zou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"issue":"12","key":"1497_CR1","doi-asserted-by":"publisher","first-page":"9709","DOI":"10.1109\/TIM.2020.3002277","volume":"69","author":"G Song","year":"2020","unstructured":"Song, G., Song, K., Yan, Y.: Edrnet: Encoder\u2013decoder residual network for salient object detection of strip steel surface defects. IEEE Trans. Instrum. Meas. 69(12), 9709\u20139719 (2020)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"6","key":"1497_CR2","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1497_CR3","doi-asserted-by":"crossref","unstructured":"Nicolas, C., Francisco, M., Gabriel, S., Nicolas, U., Alexander, K., Sergey, Z.: End-to-end object detection with transformers. In: Andrea V., Horst B., Thomas B., Jan-Michael F. (eds) Computer Vision\u2014ECCV 2020, pages 213\u2013229, Cham, 2020. Springer International Publishing","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"1497_CR4","first-page":"1","volume":"72","author":"J Ding","year":"2023","unstructured":"Ding, J., Ye, C., Huaizhi Wang, J., Huyan, M.Y., Li, W.: Foreign bodies detector based on detr for high-resolution x-ray images of textiles. IEEE Trans. Instrum. Meas. 72, 1\u201310 (2023)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"1497_CR5","unstructured":"Joseph, R., Ali, F.: Yolov3: An incremental improvement, 2018"},{"key":"1497_CR6","doi-asserted-by":"crossref","unstructured":"Glenn, J.: yolov5: v7.1. https:\/\/github.com\/ultralytics\/yolov5, 2022","DOI":"10.1155\/2022\/8900734"},{"key":"1497_CR7","unstructured":"Chien-Yao, W., Alexey, B., Hong-Yuan\u00a0Mark, L.: Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, 2022"},{"key":"1497_CR8","unstructured":"Chaurasia, A., Jocher, G., Qiu, J.: YOLO by Ultralytics. https:\/\/github.com\/ultralytics\/ultralytics, 2023"},{"key":"1497_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119560","volume":"217","author":"J Ding","year":"2023","unstructured":"Ding, J., Li, W., Pei, L., Yang, M., Ye, C., Yuan, B.: Sw-yolox: An anchor-free detector based transformer for sea surface object detection. Expert Syst. Appl. 217, 119560 (2023)","journal-title":"Expert Syst. Appl."},{"key":"1497_CR10","doi-asserted-by":"crossref","unstructured":"Sheetal, I., Swapnil, J.: Edge detection of license plate using sobel operator. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), pages 3561\u20133563, 2016","DOI":"10.1109\/ICEEOT.2016.7755367"},{"issue":"3","key":"1497_CR11","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1109\/TIM.2012.2218677","volume":"62","author":"S Ghorai","year":"2013","unstructured":"Ghorai, S., Mukherjee, A., Gangadaran, M., Dutta, P.K.: Automatic defect detection on hot-rolled flat steel products. IEEE Trans. Instrum. Meas. 62(3), 612\u2013621 (2013)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"1497_CR12","doi-asserted-by":"crossref","unstructured":"Kou, X., Liu, S., Cheng, K., Qian, Y.: Development of a yolo-v3-based model for detecting defects on steel strip surface. Measurement, (1\u20134):109454, 2021","DOI":"10.1016\/j.measurement.2021.109454"},{"key":"1497_CR13","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/JSEN.2022.3226934","volume":"22","author":"L Guo","year":"2022","unstructured":"Guo, L.: Msft-yolo: Improved yolov5 based on transformer for detecting defects of steel surface. Sensors 22, 2 (2022)","journal-title":"Sensors"},{"key":"1497_CR14","doi-asserted-by":"publisher","first-page":"133936","DOI":"10.1109\/ACCESS.2022.3230894","volume":"10","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Wang, H., Xin, Z.: Efficient detection model of steel strip surface defects based on yolo-v7. IEEE Access 10, 133936\u2013133944 (2022)","journal-title":"IEEE Access"},{"key":"1497_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118269","volume":"209","author":"H \u00dczen","year":"2022","unstructured":"\u00dczen, H., T\u00fcrko\u011flu, M., Yanikoglu, B., Hanbay, D.: Swin-mfinet: Swin transformer based multi-feature integration network for detection of pixel-level surface defects. Expert Syst. Appl. 209, 118269 (2022)","journal-title":"Expert Syst. Appl."},{"issue":"5","key":"1497_CR16","doi-asserted-by":"publisher","DOI":"10.1049\/sil2.12208","volume":"17","author":"F Meixia","year":"2023","unstructured":"Meixia, F., Jiansheng, W., Wang, Q., Sun, L., Ma, Z., Zhang, C., Guan, W., Li, W., Chen, N., Wang, D., Wang, J.: Region-based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial internet of things. IET Signal Proc. 17(5), e12208 (2023)","journal-title":"IET Signal Proc."},{"key":"1497_CR17","first-page":"12","volume":"23","author":"W Ji","year":"2023","unstructured":"Ji, W., Peiquan, X., Leijun, L., Feng, Z.: Dassd-net: A lightweight steel surface defect detection model based on multi-branch dilated convolution aggregation and multi-domain perception detection head. Sensors 23, 12 (2023)","journal-title":"Sensors"},{"key":"1497_CR18","first-page":"14","volume":"23","author":"H Zhao","year":"2023","unstructured":"Zhao, H., Wan, F., Lei, G., Xiong, Y., Li, X., Chengzhi, X., Zhou, W.: Lsd-yolov5: A steel strip surface defect detection algorithm based on lightweight network and enhanced feature fusion mode. Sensors 23, 14 (2023)","journal-title":"Sensors"},{"key":"1497_CR19","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s11554-023-01333-4","volume":"20","author":"J Liu","year":"2023","unstructured":"Liu, J., Cui, G., Xiao, C.: A real-time and efficient surface defect detection method based on yolov4. J. Real-Time Image Process. 20, 5 (2023)","journal-title":"J. Real-Time Image Process."},{"key":"1497_CR20","first-page":"58","volume":"20","author":"F Li","year":"2023","unstructured":"Li, F., Xiao, K., Zhengpeng, H., Zhang, G.: Fabric defect detection algorithm based on improved yolov5. Vis. Comput. 20, 58 (2023)","journal-title":"Vis. Comput."},{"key":"1497_CR21","first-page":"886","volume":"52","author":"G Liu","year":"2023","unstructured":"Liu, G., Ren, J.: Feature purification fusion structure for fabric defect detection. Vis. Comput. 52, 886 (2023)","journal-title":"Vis. Comput."},{"key":"1497_CR22","first-page":"96","volume":"53","author":"Z Chen","year":"2024","unstructured":"Chen, Z., Huang, S., Lv, H., Luo, Z., Liu, J.: Defect detection in automotive glass based on modified yolov5 with multi-scale feature fusion and dual lightweight strategy. Vis. Comput. 53, 96 (2024)","journal-title":"Vis. Comput."},{"issue":"2","key":"1497_CR23","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s00371-023-02793-2","volume":"40","author":"W Hou","year":"2024","unstructured":"Hou, W., Jing, H.: Rc-yolov5s: for tile surface defect detection. Vis. Comput. 40(2), 459\u2013470 (2024)","journal-title":"Vis. Comput."},{"issue":"2","key":"1497_CR24","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","volume":"43","author":"S-H Gao","year":"2021","unstructured":"Gao, S.-H., Cheng, M.-M., Zhao, K., Zhang, X.-Y., Yang, M.-H., Torr, P.: Res2net: A new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 652\u2013662 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1497_CR25","unstructured":"Qibin, H., Daquan, Z., Jiashi, F.: Coordinate attention for efficient mobile network design. In 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 13708\u201313717, 2021"},{"key":"1497_CR26","unstructured":"Tong, Z., Chen, Y., Zewei, X.: Bounding box regression loss with dynamic focusing mechanism, Wise-iou (2023)"},{"key":"1497_CR27","unstructured":"Pavlo, M., Arun, M., Stephen, T., Iuri, F., Jan, K.: Importance estimation for neural network pruning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019"},{"key":"1497_CR28","doi-asserted-by":"crossref","unstructured":"Changyong, S., Yifan, L., Jianfei, G., Zheng, Y., Chunhua, S.: Channel-wise knowledge distillation for dense prediction. In 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pages 5291\u20135300, 2021","DOI":"10.1109\/ICCV48922.2021.00526"},{"issue":"8","key":"1497_CR29","first-page":"858","volume":"285","author":"S Kechen","year":"2013","unstructured":"Kechen, S., Yunhui, Y.: A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl. Surf. Sci. 285(8), 858\u2013864 (2013)","journal-title":"Appl. Surf. Sci."},{"key":"1497_CR30","first-page":"6","volume":"20","author":"L Xiaoming","year":"2020","unstructured":"Xiaoming, L., Fajie, D., Jia-jia, J., Xiao, F., Lin, G.: Deep metallic surface defect detection: The new benchmark and detection network. Sensors 20, 6 (2020)","journal-title":"Sensors"},{"key":"1497_CR31","first-page":"1","volume":"72","author":"H Chen","year":"2023","unstructured":"Chen, H., Yongzhao, D., Yuqing, F., Zhu, J., Zeng, H.: Dcam-net: A rapid detection network for strip steel surface defects based on deformable convolution and attention mechanism. IEEE Trans. Instrum. Meas. 72, 1\u201312 (2023)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"4","key":"1497_CR32","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1109\/TIM.2019.2915404","volume":"69","author":"Yu He","year":"2020","unstructured":"He, Yu., Song, K., Meng, Q., Yan, Y.: An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Trans. Instrum. Meas. 69(4), 1493\u20131504 (2020)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"1497_CR33","unstructured":"Yian, Z., Wenyu, L., Shangliang, X., Jinman, W., Guanzhong, W., Qingqing, D., Yi, L., Jie, C.: Detrs beat yolos on real-time object detection, 2024"},{"key":"1497_CR34","doi-asserted-by":"crossref","unstructured":"Hou, X., Liu, M., Zhang, S., Wei, P.: Enhancing detection transformer with hierarchical salience filtering refinement, Salience detr (2024)","DOI":"10.1109\/CVPR52733.2024.01664"},{"issue":"1","key":"1497_CR35","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1007\/s40747-023-01180-7","volume":"10","author":"L Zhaoguo","year":"2024","unstructured":"Zhaoguo, L., Xiumei, W., Hassaballah, M., Yihong, L., Xuesong, J.: A deep learning model for steel surface defect detection. Compl. Intell. Syst. 10(1), 885\u2013897 (2024)","journal-title":"Compl. Intell. Syst."},{"issue":"1","key":"1497_CR36","doi-asserted-by":"publisher","first-page":"7671","DOI":"10.1038\/s41598-024-57990-3","volume":"14","author":"H Zhang","year":"2024","unstructured":"Zhang, H., Li, S., Miao, Q., Fang, R., Xue, S., Qianchuan, H., Jie, H., Chan, S.: Surface defect detection of hot rolled steel based on multi-scale feature fusion and attention mechanism residual block. Sci. Rep. 14(1), 7671 (2024)","journal-title":"Sci. Rep."},{"key":"1497_CR37","first-page":"6","volume":"20","author":"L Xiaoming","year":"2020","unstructured":"Xiaoming, L., Fajie, D., Jia-jia, J., Xiao, F., Lin, G.: Deep metallic surface defect detection: The new benchmark and detection network. Sensors 20, 6 (2020)","journal-title":"Sensors"},{"key":"1497_CR38","unstructured":"Jie, H., Li, S., Gang, S.: Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7132\u20137141, 2018"},{"key":"1497_CR39","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., In So, K.: Convolutional block attention module, Cbam (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1497_CR40","unstructured":"Yann, L., John, D., Sara, S.: Optimal brain damage. In D.\u00a0Touretzky, editor, Advances in Neural Information Processing Systems, volume\u00a02. Morgan-Kaufmann, 1989"},{"key":"1497_CR41","unstructured":"Jaeho, L., Sejun, P., Sangwoo, M., Sungsoo, A., Jinwoo, S.: A deeper look at the layerwise sparsity of magnitude-based pruning. CoRR, abs\/2010.07611, 2020"},{"key":"1497_CR42","doi-asserted-by":"crossref","unstructured":"Gongfan, F., Xinyin, M., Mingli, S., Michael\u00a0Bi, M., Xinchao, W.: Depgraph: Towards any structural pruning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 16091\u201316101, 2023","DOI":"10.1109\/CVPR52729.2023.01544"},{"key":"1497_CR43","doi-asserted-by":"crossref","unstructured":"Wei, Y., Pan, X., Qin, H., Ouyang, W., Junjie, Y.: Towards very tiny cnn for object detection, Quantization mimic (2018)","DOI":"10.1007\/978-3-030-01237-3_17"},{"key":"1497_CR44","doi-asserted-by":"crossref","unstructured":"Zhendong, Y., Zhe, L., Mingqi, S., Dachuan, S., Zehuan, Y., Chun, Y.: Masked generative distillation. In: Shai, A., Gabriel, B., Moustapha, C., Giovanni\u00a0Maria, F., Tal, H., editors, Computer Vision \u2013 ECCV 2022, pages 53\u201369, Cham, 2022. Springer Nature Switzerland","DOI":"10.1007\/978-3-031-20083-0_4"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01497-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01497-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01497-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T06:30:23Z","timestamp":1732257023000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01497-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,19]]},"references-count":44,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["1497"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01497-7","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,19]]},"assertion":[{"value":"17 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 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 declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"117"}}