{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T21:00:52Z","timestamp":1784754052229,"version":"3.55.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Basic Research Project of Liaoning Provincial Department of Education","award":["LJ212411258030"],"award-info":[{"award-number":["LJ212411258030"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1007\/s11554-026-01941-w","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T20:01:10Z","timestamp":1784750470000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel attention-guided inspection approach for minute industrial surface defect based on separate adaptive data augmentation"],"prefix":"10.1007","volume":"23","author":[{"given":"Jinghui","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,22]]},"reference":[{"issue":"4","key":"1941_CR1","doi-asserted-by":"publisher","first-page":"3506","DOI":"10.1109\/TIE.2020.2982115","volume":"68","author":"L Xie","year":"2020","unstructured":"Xie, L., Xiang, X., Xu, H., Wang, L., Lin, L., Yin, G.: FFCNN: A deep neural network for surface defect detection of magnetic tile. IEEE Trans. Ind. Electron. 68(4), 3506\u20133516 (2020)","journal-title":"IEEE Trans. Ind. Electron."},{"key":"1941_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111631","volume":"159","author":"J Li","year":"2024","unstructured":"Li, J., Wang, K., He, M., Ke, L., Wang, H.: Attention-based convolution neural network for magnetic tile surface defect classification and detection. Appl. Soft Comput. 159, 111631 (2024)","journal-title":"Appl. Soft Comput."},{"issue":"9","key":"1941_CR3","doi-asserted-by":"publisher","first-page":"4454","DOI":"10.1080\/10589759.2024.2419895","volume":"40","author":"C Jiang","year":"2025","unstructured":"Jiang, C., Xu, B., Li, Z., Zhang, L., Sun, Q., Zhang, D.: MTGGAN: Generate type-controllable magnetic tile defect data to improve defect detection performance. Nondestruct. Test. Eval. 40(9), 4454\u20134482 (2025)","journal-title":"Nondestruct. Test. Eval."},{"key":"1941_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlastec.2025.113956","volume":"192","author":"P Shi","year":"2025","unstructured":"Shi, P., Jiang, H., Li, X., Shang, X., Jiang, J., Huang, B., Zhao, R., Zhu, W.: Surface defect detection of magnetic tile based on RT-DETR improved algorithm. Opt. Laser Technol. 192, 113956 (2025)","journal-title":"Opt. Laser Technol."},{"key":"1941_CR5","doi-asserted-by":"crossref","unstructured":"Haobo, Y.: A survey of industrial surface defect detection based on deep learning. In: Proc. 2024 Int. Conf. Cyber-Phys. Soc. Intell. (ICCSI), pp. 1\u20136. IEEE (2024)","DOI":"10.1109\/ICCSI62669.2024.10799405"},{"key":"1941_CR6","first-page":"1","volume":"70","author":"L Cui","year":"2021","unstructured":"Cui, L., Jiang, X., Xu, M., Li, W., Lv, P., Zhou, B.: SDDNet: A fast and accurate network for surface defect detection. IEEE Trans. Instrum. Meas. 70, 1\u201313 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"12","key":"1941_CR7","doi-asserted-by":"publisher","first-page":"7448","DOI":"10.1109\/TII.2019.2958826","volume":"16","author":"H Dong","year":"2019","unstructured":"Dong, H., Song, K., He, Y., Xu, J., Yan, Y., Meng, Q.: PGA-Net: Pyramid feature fusion and global context attention network for automated surface defect detection. IEEE Trans. Ind. Inform. 16(12), 7448\u20137458 (2019)","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"12","key":"1941_CR8","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-decoder 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."},{"key":"1941_CR9","doi-asserted-by":"crossref","unstructured":"Yang, Y., Dai, J., Wang, Y., Chen, Y.: Fm-rtdetr: Small object detection algorithm based on enhanced feature fusion with mamba. IEEE Signal Process. Lett., (2025)","DOI":"10.1109\/LSP.2025.3553426"},{"issue":"9","key":"1941_CR10","doi-asserted-by":"publisher","first-page":"17554","DOI":"10.3934\/mbe.2023779","volume":"20","author":"F Luo","year":"2023","unstructured":"Luo, F., Cui, Y., Wang, X., Zhang, Z., Liao, Y.: Adaptive rotation attention network for accurate defect detection on magnetic tile surface. Math. Biosci. Eng. 20(9), 17554\u201317568 (2023)","journal-title":"Math. Biosci. Eng."},{"key":"1941_CR11","first-page":"1","volume":"71","author":"W Liang","year":"2022","unstructured":"Liang, W., Sun, Y.: ELCNN: A deep neural network for small object defect detection of magnetic tile. IEEE Trans. Instrum. Meas. 71, 1\u201310 (2022)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"1941_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.mtcomm.2024.110480","volume":"41","author":"C Jiang","year":"2024","unstructured":"Jiang, C., Zhang, X., Xu, B., Zheng, Q., Li, Z., Zhang, L., Zhang, D.: MT-U2Net: Lightweight detection network for high-precision magnetic tile surface defect localization. Mater. Today Commun. 41, 110480 (2024)","journal-title":"Mater. Today Commun."},{"issue":"1","key":"1941_CR13","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s00371-018-1588-5","volume":"36","author":"Y Huang","year":"2020","unstructured":"Huang, Y., Qiu, C., Yuan, K.: Surface defect saliency of magnetic tile. Vis. Comput. 36(1), 85\u201396 (2020)","journal-title":"Vis. Comput."},{"issue":"2","key":"1941_CR14","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":"14","key":"1941_CR15","doi-asserted-by":"publisher","first-page":"2857","DOI":"10.3390\/electronics14142857","volume":"14","author":"C Ma","year":"2025","unstructured":"Ma, C., Pan, Y., Chen, J.: Surface defect detection of magnetic tiles based on YOLOv8-AHF. Electronics 14(14), 2857 (2025)","journal-title":"Electronics"},{"key":"1941_CR16","doi-asserted-by":"crossref","unstructured":"Fan, D., Ge, H., Li, J., Shen, J., Gao, J.: YOLO-MT: Real-time and efficient surface defect detection method for magnetic sheet. Nondestruct. Test. Eval, 1\u201328 (2025)","DOI":"10.1080\/10589759.2025.2589470"},{"key":"1941_CR17","first-page":"1","volume":"71","author":"X Cao","year":"2022","unstructured":"Cao, X., Chen, B., He, W.: Unsupervised defect segmentation of magnetic tile based on attention enhanced flexible U-Net. IEEE Trans. Instrum. Meas. 71, 1\u201310 (2022)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"11","key":"1941_CR18","doi-asserted-by":"publisher","first-page":"2413","DOI":"10.1177\/0142331220982220","volume":"43","author":"M Ben Gharsallah","year":"2021","unstructured":"Ben Gharsallah, M., Ben Braiek, E.: Defect identification in magnetic tile images using an improved nonlinear diffusion method. Trans. Inst. Meas. Control. 43(11), 2413\u20132424 (2021)","journal-title":"Trans. Inst. Meas. Control."},{"key":"1941_CR19","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 4183\u20134192. (2020)","DOI":"10.1109\/CVPR42600.2020.00424"},{"issue":"2","key":"1941_CR20","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/s11554-024-01445-5","volume":"21","author":"S Cai","year":"2024","unstructured":"Cai, S., Meng, H., Wu, J.: Fe-yolo: Yolo ship detection algorithm based on feature fusion and feature enhancement. J. Real-Time Image Proc. 21(2), 61 (2024)","journal-title":"J. Real-Time Image Proc."},{"issue":"2","key":"1941_CR21","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s11554-024-01419-7","volume":"21","author":"S Bagherzadeh","year":"2024","unstructured":"Bagherzadeh, S., Daryanavard, H., Semati, M.R.: A novel multiplier-less convolution core for yolo cnn asic implementation. J. Real-Time Image Proc. 21(2), 45 (2024)","journal-title":"J. Real-Time Image Proc."},{"issue":"1","key":"1941_CR22","doi-asserted-by":"publisher","first-page":"27279","DOI":"10.1038\/s41598-025-12339-2","volume":"15","author":"J Tang","year":"2025","unstructured":"Tang, J., Zhang, A., Liu, W.: A novel dual-student reverse knowledge distillation method for magnetic tile defect detection. Sci. Rep. 15(1), 27279 (2025)","journal-title":"Sci. Rep."},{"issue":"5","key":"1941_CR23","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1007\/s12204-019-2101-7","volume":"24","author":"D Li","year":"2019","unstructured":"Li, D., Niu, Z., Peng, D.: Magnetic tile surface defect detection based on texture feature clustering. J. Shanghai Jiaotong Univ. (Sci.) 24(5), 663\u2013670 (2019)","journal-title":"J. Shanghai Jiaotong Univ. (Sci.)"},{"issue":"4","key":"1941_CR24","doi-asserted-by":"publisher","DOI":"10.1088\/2631-8695\/ae13dd","volume":"7","author":"X Shang","year":"2025","unstructured":"Shang, X., Li, X., Shi, P., Jiang, J.: Surface defect detection of magnetic tiles based on an improved lightweight GhostNet network. Eng. Res. Express 7(4), 045255 (2025)","journal-title":"Eng. Res. Express"},{"key":"1941_CR25","first-page":"1","volume":"72","author":"J Liu","year":"2023","unstructured":"Liu, J., Li, H., Zuo, F., Zhao, Z., Lu, S.: KD-LightNet: A lightweight network based on knowledge distillation for industrial defect detection. IEEE Trans. Instrum. Meas. 72, 1\u201313 (2023)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"1941_CR26","unstructured":"Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: Yolov4: Optimal speed and accuracy of object detection, (2020). arXiv preprint arXiv:2004.10934"},{"key":"1941_CR27","doi-asserted-by":"crossref","unstructured":"Chaman, M., El Maliki, A., Jariri, N., El Yanboiy, H., El Mrabet, A., Dahou, H., La\u00e2mari, H., Hadjoudja, A.: Real-time vehicle detection and instance segmentation for ADAS using the YOLOv11-SEG model: A deep learning approach. In: Proc. 2025 Int. Conf. Circuit, Syst. Commun. (ICCSC), pp. 1\u20136. IEEE (2025)","DOI":"10.1109\/ICCSC66714.2025.11135303"},{"key":"1941_CR28","unstructured":"Sapkota, R., Karkee, M.: Ultralytics YOLO evolution: An overview of YOLO26, YOLO11, YOLOv8 and YOLOv5 object detectors for computer vision and pattern recognition, (2025). arXiv preprint arXiv:2510.09653"},{"key":"1941_CR29","unstructured":"Jin, Z., Dong, L.: YOLO11-CR: A lightweight convolution-and-attention framework for accurate fatigue driving detection, (2025). arXiv preprint arXiv:2508.13205"},{"key":"1941_CR30","doi-asserted-by":"crossref","unstructured":"Liu, X., Peng, H., Zheng, N., Yang, Y., Hu, H., Yuan, Y.: EfficientViT: Memory efficient vision transformer with cascaded group attention. In: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 14420\u201314430. (2023)","DOI":"10.1109\/CVPR52729.2023.01386"},{"key":"1941_CR31","unstructured":"Williams, T., Li, R.: Wavelet pooling for convolutional neural networks. In: Proc. Int. Conf. Learn. Represent. (ICLR), (2018)"},{"key":"1941_CR32","doi-asserted-by":"crossref","unstructured":"Yu, Z., Huang, H., Chen, W., Su, Y., Liu, Y., Wang, X.: YOLO-FaceV2: A scale and occlusion aware face detector. Pattern Recognit. 155, 110714 (2024)","DOI":"10.1016\/j.patcog.2024.110714"},{"issue":"2","key":"1941_CR33","doi-asserted-by":"publisher","first-page":"125","DOI":"10.3390\/info11020125","volume":"11","author":"A Buslaev","year":"2020","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020)","journal-title":"Information"},{"key":"1941_CR34","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626. (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"1941_CR35","unstructured":"Team, U.: YOLOv11: Real-time Object Detection and Instance Segmentation. GitHub repository. Accessed 16 Apr 2026 (2024). https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"1941_CR36","unstructured":"Jocher, G.: YOLOv5: Real-time Object Detection. GitHub repository. Accessed 16 Apr 2026 (2020). https:\/\/github.com\/ultralytics\/yolov5"},{"key":"1941_CR37","unstructured":"Ultralytics: YOLOv8: State-of-the-art Object Detection. GitHub repository. Accessed 16 Apr 2026 (2023). https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"1941_CR38","unstructured":"Tian, Y., Ye, Q., Doermann, D.: Yolov12: Attention-centric real-time object detectors, (2025). arXiv preprint arXiv:2502.12524"},{"key":"1941_CR39","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: European Conference on Computer Vision, pp. 21\u201337. Springer (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1941_CR40","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. ArXiv (Cornell University) (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"1941_CR41","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: European Conference on Computer Vision, pp. 213\u2013229. Springer (2020)","DOI":"10.1007\/978-3-030-58452-8_13"},{"issue":"3","key":"1941_CR42","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1007\/s10845-019-01476-x","volume":"31","author":"D Tabernik","year":"2020","unstructured":"Tabernik, D., \u0160ela, S., Skvar\u010d, J., Sko\u010daj, D.: Segmentation-based deep-learning approach for surface-defect detection. J. Intell. Manuf. 31(3), 759\u2013776 (2020)","journal-title":"J. Intell. Manuf."}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-026-01941-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-026-01941-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-026-01941-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T20:01:27Z","timestamp":1784750487000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-026-01941-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,22]]},"references-count":42,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["1941"],"URL":"https:\/\/doi.org\/10.1007\/s11554-026-01941-w","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,22]]},"assertion":[{"value":"21 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no conflict of interest. The authors declare no competing interests.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The code will be made publicly available upon acceptance of this manuscript.","order":3,"name":"Ethics","label":"Code availability","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":4,"name":"Ethics","label":"Ethics approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":5,"name":"Ethics","label":"Materials availability","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"139"}}