{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T03:50:40Z","timestamp":1767844240025,"version":"3.49.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T00:00:00Z","timestamp":1678406400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T00:00:00Z","timestamp":1678406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"national key research and development program of china","award":["2019YFB1312600"],"award-info":[{"award-number":["2019YFB1312600"]}]},{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["52075480"],"award-info":[{"award-number":["52075480"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"high-level talent special support plan of zhejiang province","award":["2020R52004"],"award-info":[{"award-number":["2020R52004"]}]},{"DOI":"10.13039\/501100004731","name":"natural science foundation of zhejiang province","doi-asserted-by":"publisher","award":["Y19E050078"],"award-info":[{"award-number":["Y19E050078"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s10845-023-02097-1","type":"journal-article","created":{"date-parts":[[2023,3,26]],"date-time":"2023-03-26T21:32:00Z","timestamp":1679866320000},"page":"1141-1157","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Hierarchical multi-scale network for cross-scale visual defect detection"],"prefix":"10.1007","volume":"35","author":[{"given":"Ruining","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiguo","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7974-7059","authenticated-orcid":false,"given":"Guifang","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianrong","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"2097_CR1","first-page":"1","volume":"70","author":"Y Bao","year":"2021","unstructured":"Bao, Y., Song, K., Liu, J., Wang, Y., Yan, Y., Yu, H., & Li, X. (2021). Triplet-graph reasoning network for few-shot metal generic surface defect segmentation. IEEE Transactions on Instrumentation and Measurement, 70, 1\u201311.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"2097_CR2","doi-asserted-by":"crossref","unstructured":"Cai, Z., & Vasconcelos, N. (2018). Cascade r-cnn: Delving into high quality object detection, in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6154\u20136162.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"2097_CR3","doi-asserted-by":"crossref","unstructured":"Cao, Y., Xu, J., Lin, S., Wei, F., & Hu, H. (2019). GCNet: Non-Local Networks Meet Squeeze-Excitation Networks and Beyond, in 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), IEEE Computer Society, pp. 1971\u20131980. IEEE Computer Society.","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"2097_CR4","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1007\/s10845-020-01704-9","volume":"33","author":"A \u00c7elik","year":"2022","unstructured":"\u00c7elik, A., K\u00fc\u00e7\u00fckmanisa, A., S\u00fcmer, A., \u00c7elebi, A. T., & Urhan, O. (2022). A real-time defective pixel detection system for lcds using deep learning based object detectors. Journal of Intelligent Manufacturing, 33, 985\u2013994.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2097_CR5","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., & Xu, J. et al. (2019). Mmdetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155, 1\u201313."},{"issue":"2","key":"2097_CR6","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1109\/TSM.2021.3065405","volume":"34","author":"KC-C Cheng","year":"2021","unstructured":"Cheng, K.C.-C., Chen, L.L.-Y., Li, J.-W., Li, K.S.-M., Tsai, N.C.-Y., Wang, S.-J., Huang, A.Y.-A., Chou, L., Lee, C.-S., Chen, J. E., et al. (2021). Machine learning-based detection method for wafer test induced defects. IEEE Transactions on Semiconductor Manufacturing, 34(2), 161\u2013167.","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"key":"2097_CR7","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database, in 2009 IEEE conference on computer vision and pattern recognition, Ieee, pp. 248\u2013255. IEEE.","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"2","key":"2097_CR8","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88(2), 303\u2013338.","journal-title":"International Journal of Computer Vision"},{"issue":"2","key":"2097_CR9","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","volume":"43","author":"S-H Gao","year":"2019","unstructured":"Gao, S.-H., Cheng, M.-M., Zhao, K., Zhang, X.-Y., Yang, M.-H., & Torr, P. (2019). Res2net: A new multi-scale backbone architecture. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(2), 652\u2013662.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"5","key":"2097_CR10","doi-asserted-by":"publisher","first-page":"3450","DOI":"10.1109\/TII.2020.3013277","volume":"17","author":"Y Gao","year":"2020","unstructured":"Gao, Y., Lin, J., Xie, J., & Ning, Z. (2020). A real-time defect detection method for digital signal processing of industrial inspection applications. IEEE Transactions on Industrial Informatics, 17(5), 3450\u20133459.","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"2097_CR11","doi-asserted-by":"crossref","unstructured":"Guo, C., Fan, B., Zhang, Q., Xiang, S., & Pan, C. (2020). Augfpn: Improving multi-scale feature learning for object detection, in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12595\u201312604.","DOI":"10.1109\/CVPR42600.2020.01261"},{"key":"2097_CR12","doi-asserted-by":"publisher","first-page":"1833","DOI":"10.1007\/s10845-020-01670-2","volume":"32","author":"R Hao","year":"2021","unstructured":"Hao, R., Lu, B., Cheng, Y., Li, X., & Huang, B. (2021). A steel surface defect inspection approach towards smart industrial monitoring. Journal of Intelligent Manufacturing, 32, 1833\u20131843.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2097_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition, in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"4","key":"2097_CR14","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1109\/TIM.2019.2915404","volume":"69","author":"Y He","year":"2019","unstructured":"He, Y., Song, K., Meng, Q., & Yan, Y. (2019). An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Transactions on Instrumentation and Measurement, 69(4), 1493\u20131504.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"2097_CR15","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1007\/s10845-020-01591-0","volume":"32","author":"C-Y Hsu","year":"2021","unstructured":"Hsu, C.-Y., & Liu, W.-C. (2021). Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing. Journal of Intelligent Manufacturing, 32, 823\u2013836.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"8","key":"2097_CR16","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2019","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2019). Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(8), 2011\u20132023.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2097_CR17","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1007\/s10845-020-01710-x","volume":"33","author":"S Jain","year":"2022","unstructured":"Jain, S., Seth, G., Paruthi, A., Soni, U., & Kumar, G. (2022). Synthetic data augmentation for surface defect detection and classification using deep learning. Journal of Intelligent Manufacturing, 33, 1007\u20131020.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2097_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.microrel.2021.114157","volume":"122","author":"Y Kim","year":"2021","unstructured":"Kim, Y., Cho, D., & Lee, J.-H. (2021). Wafer defect pattern classification with detecting out-of-distribution. Microelectronics Reliability, 122, 114157.","journal-title":"Microelectronics Reliability"},{"key":"2097_CR19","doi-asserted-by":"crossref","unstructured":"Kong, T., Yao, A., Chen, Y., & Sun, F. (2016). Hypernet: Towards accurate region proposal generation and joint object detection, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 845\u2013853.","DOI":"10.1109\/CVPR.2016.98"},{"key":"2097_CR20","first-page":"1","volume":"70","author":"D Li","year":"2021","unstructured":"Li, D., Li, Y., Xie, Q., Wu, Y., Yu, Z., & Wang, J. (2021). Tiny defect detection in high-resolution aero-engine blade images via a coarse-to-fine framework. IEEE Transactions on Instrumentation and Measurement, 70, 1\u201312.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"2097_CR21","first-page":"1","volume":"70","author":"F Li","year":"2021","unstructured":"Li, F., & Xi, Q. (2021). Defectnet: Toward fast and effective defect detection. IEEE Transactions on Instrumentation and Measurement, 70, 1\u20139.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"2097_CR22","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, Y., Wang, N., & Zhang, Z. (2019). Scale-aware trident networks for object detection, in Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 6054\u20136063.","DOI":"10.1109\/ICCV.2019.00615"},{"key":"2097_CR23","doi-asserted-by":"crossref","unstructured":"Lin, T-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., & Zitnick, C.L. (2014). Microsoft coco: Common objects in context, in European conference on computer vision, Springer, 2014, pp. 740\u2013755. Springer.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"2097_CR24","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017a) Feature pyramid networks for object detection, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017a, pp. 2117\u20132125.","DOI":"10.1109\/CVPR.2017.106"},{"key":"2097_CR25","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Doll\u00e1r, P. (2017b). Focal loss for dense object detection, in Proceedings of the IEEE international conference on computer vision, pp. 2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"2097_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3154814","volume":"71","author":"Z Ling","year":"2022","unstructured":"Ling, Z., Zhang, A., Ma, D., Shi, Y., & Wen, H. (2022). Deep Siamese semantic segmentation network for pcb welding defect detection. IEEE Transactions on Instrumentation and Measurement, 71, 1\u201311.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"2097_CR27","doi-asserted-by":"crossref","unstructured":"Liu, J.-J., Hou, Q., Cheng, M.-M., Wang, C., & Feng, J. (2020a). Improving convolutional networks with self-calibrated convolutions, in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10096\u201310105.","DOI":"10.1109\/CVPR42600.2020.01011"},{"key":"2097_CR28","doi-asserted-by":"publisher","first-page":"1813","DOI":"10.1007\/s10845-020-01536-7","volume":"31","author":"R Liu","year":"2020","unstructured":"Liu, R., Sun, Z., Wang, A., Yang, K., Wang, Y., & Sun, Q. (2020). Real-time defect detection network for polarizer based on deep learning. Journal of Intelligent Manufacturing, 31, 1813\u20131823.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2097_CR29","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018). Path aggregation network for instance segmentation, in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8759\u20138768.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"2097_CR30","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A.C. (2016). Ssd: Single shot multibox detector, in European conference on computer vision, Springer, pp. 21\u201337. Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"12","key":"2097_CR31","doi-asserted-by":"publisher","first-page":"9681","DOI":"10.1109\/TIM.2020.3001695","volume":"69","author":"Z Liu","year":"2020","unstructured":"Liu, Z., Yang, B., Duan, G., & Tan, J. (2020). Visual defect inspection of metal part surface via deformable convolution and concatenate feature pyramid neural networks. IEEE Transactions on Instrumentation and Measurement, 69(12), 9681\u20139694.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"2097_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlaseng.2020.106423","volume":"138","author":"Z Liu","year":"2021","unstructured":"Liu, Z., Tang, R., Duan, G., & Tan, J. (2021). Truingdet: Towards high-quality visual automatic defect inspection for mental surface. Optics and Lasers in Engineering, 138, 106423.","journal-title":"Optics and Lasers in Engineering"},{"key":"2097_CR33","doi-asserted-by":"crossref","unstructured":"Liu, Z., Song, Y., Tang, R., Duan, G., & Tan, J. (2022). Few-shot defect recognition of metal surfaces via attention-embedding and self-supervised learning. Journal of Intelligent Manufacturing, 1\u201315.","DOI":"10.1007\/s10845-022-02022-y"},{"key":"2097_CR34","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1007\/s10845-020-01607-9","volume":"32","author":"S Meng","year":"2021","unstructured":"Meng, S., Pan, R., Gao, W., Zhou, J., Wang, J., & He, W. (2021). A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern. Journal of Intelligent Manufacturing, 32, 1147\u20131161.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2097_CR35","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., & Lin, D. (2019) 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.","DOI":"10.1109\/CVPR.2019.00091"},{"key":"2097_CR36","doi-asserted-by":"crossref","unstructured":"Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., & Doll\u00e1r, P. (2020). Designing network design spaces, in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10428\u201310436.","DOI":"10.1109\/CVPR42600.2020.01044"},{"issue":"06","key":"2097_CR37","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. (2017). Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 39(06), 1137\u20131149.","journal-title":"IEEE Transactions on Pattern Analysis & Machine Intelligence"},{"issue":"4","key":"2097_CR38","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1007\/s10845-021-01906-9","volume":"33","author":"T Schlosser","year":"2022","unstructured":"Schlosser, T., Friedrich, M., Beuth, F., & Kowerko, D. (2022). Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks. Journal of Intelligent Manufacturing, 33(4), 1099\u20131123.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2097_CR39","doi-asserted-by":"crossref","unstructured":"Singh, B., & Davis, L.S. (2018) An analysis of scale invariance in object detection snip, in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3578\u20133587.","DOI":"10.1109\/CVPR.2018.00377"},{"key":"2097_CR40","first-page":"1","volume":"31","author":"B Singh","year":"2018","unstructured":"Singh, B., Najibi, M., & Davis, L. S. (2018). Sniper: Efficient multi-scale training. Advances in Neural Information Processing Systems, 31, 1\u201310.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2097_CR41","first-page":"1","volume":"70","author":"Y Song","year":"2021","unstructured":"Song, Y., Liu, Z., Wang, J., Tang, R., Duan, G., & Tan, J. (2021). Multiscale adversarial and weighted gradient domain adaptive network for data scarcity surface defect detection. IEEE Transactions on Instrumentation and Measurement, 70, 1\u201310.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"2097_CR42","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s10845-020-01563-4","volume":"32","author":"ML Stern","year":"2021","unstructured":"Stern, M. L., & Schellenberger, M. (2021). Fully convolutional networks for chip-wise defect detection employing photoluminescence images: Efficient quality control in led manufacturing. Journal of Intelligent Manufacturing, 32, 113\u2013126.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2097_CR43","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., & Wang, J. (2019). Deep high-resolution representation learning for human pose estimation, in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5693\u20135703.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"2097_CR44","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia,Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions, in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2097_CR45","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision, in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818\u20132826.","DOI":"10.1109\/CVPR.2016.308"},{"key":"2097_CR46","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning, in Proceedings of the AAAI conference on artificial intelligence, vol. 31, pp. 1\u20138.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"2097_CR47","doi-asserted-by":"crossref","unstructured":"Tang, R., Liu, Z., Li, Y., Song, Y., Liu,H., Wang, Q., Shao, J., Duan, G., & Tan, J. (2023). Task-balanced distillation for object detection. Pattern Recognition, 109320.","DOI":"10.1016\/j.patcog.2023.109320"},{"key":"2097_CR48","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., & He, T. (2019). Fcos: Fully convolutional one-stage object detection, in Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9627\u20139636.","DOI":"10.1109\/ICCV.2019.00972"},{"issue":"10","key":"2097_CR49","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2020","unstructured":"Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al. (2020). Deep high-resolution representation learning for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(10), 3349\u20133364.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2097_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2020.101037","volume":"43","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Liu, M., Zheng, P., Yang, H., & Zou, J. (2020). A smart surface inspection system using faster r-cnn in cloud-edge computing environment. Advanced Engineering Informatics, 43, 101037.","journal-title":"Advanced Engineering Informatics"},{"key":"2097_CR51","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., & Kweon, I.S. (2018). Cbam: Convolutional block attention module, in Proceedings of the European conference on computer vision (ECCV), pp. 3\u201319.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"2097_CR52","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., & He, K. (2017). Aggregated residual transformations for deep neural networks, in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1492\u20131500.","DOI":"10.1109\/CVPR.2017.634"},{"issue":"10","key":"2097_CR53","doi-asserted-by":"publisher","first-page":"6743","DOI":"10.1109\/TII.2021.3126098","volume":"18","author":"B Yang","year":"2021","unstructured":"Yang, B., Liu, Z., Duan, G., & Tan, J. (2021). Mask2defect: A prior knowledge-based data augmentation method for metal surface defect inspection. IEEE Transactions on Industrial Informatics, 18(10), 6743\u20136755.","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"2097_CR54","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., & Li, S.Z. (2020). Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection, in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9759\u20139768.","DOI":"10.1109\/CVPR42600.2020.00978"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02097-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-023-02097-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02097-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T19:06:15Z","timestamp":1709147175000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-023-02097-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,10]]},"references-count":54,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["2097"],"URL":"https:\/\/doi.org\/10.1007\/s10845-023-02097-1","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,10]]},"assertion":[{"value":"3 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2023","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}