{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T10:29:00Z","timestamp":1745490540294,"version":"3.37.3"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"R &D projects in key areas of Guangdong Province","award":["2018B010109007","2019B010153002"],"award-info":[{"award-number":["2018B010109007","2019B010153002"]}]},{"name":"Guangdong Provincial Key Laboratory of Cyber-Physical System","award":["2020B1212060069"],"award-info":[{"award-number":["2020B1212060069"]}]},{"DOI":"10.13039\/501100014857","name":"National Natural Science Foundation of China Guangdong Joint Fund","doi-asserted-by":"crossref","award":["U1801263","U2001201"],"award-info":[{"award-number":["U1801263","U2001201"]}],"id":[{"id":"10.13039\/501100014857","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Guangzhou R &D Programme in Key Areas of Science and Technology Projects","award":["202007040006"],"award-info":[{"award-number":["202007040006"]}]},{"name":"Program of Marine Economy Development (Six Marine Industries) Special Foundation of Department of Natural Resources of Guangdong Province","award":["GDNGC [2020]056"],"award-info":[{"award-number":["GDNGC [2020]056"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s00371-022-02554-7","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T12:05:54Z","timestamp":1655467554000},"page":"3995-4013","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Bi-deformation-UNet: recombination of differential channels for printed surface defect detection"],"prefix":"10.1007","volume":"39","author":[{"given":"Ziyang","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3640-3229","authenticated-orcid":false,"given":"Guoheng","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Ying","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Junhao","family":"Qiu","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Zhiwen","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Chi-Man","family":"Pun","sequence":"additional","affiliation":[]},{"given":"Wing-Kuen","family":"Ling","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,17]]},"reference":[{"key":"2554_CR1","unstructured":"Li, X., Woo, W.L., Gu, L., Qiu, X.: Quantitative surface crack evaluation based on eddy current pulsed thermography. IEEE Sensors J., pp. 1\u20131 (2016)"},{"key":"2554_CR2","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.apsusc.2015.05.033","volume":"349","author":"XC Yuan","year":"2015","unstructured":"Yuan, X.C., Wu, L.S., Peng, Q.: An improved Otsu method using the weighted object variance for defect detection. Appl. Surf. Sci. 349, 472\u2013484 (2015)","journal-title":"Appl. Surf. Sci."},{"key":"2554_CR3","doi-asserted-by":"crossref","unstructured":"Win, M., Bushroa, A.R., Hassan, M., Nordin, H., Ide-Ektessabi, A.: A contrast adjustment thresholding method for surface defect detection based on mesoscopy. IEEE Trans. Ind. Inform. 11 (2015)","DOI":"10.1109\/TII.2015.2417676"},{"key":"2554_CR4","doi-asserted-by":"crossref","unstructured":"Wakaf, Z., Jalab, H.: Defect detection based on extreme edge of defective region histogram. J. King Saud Univ. - Comput. Inf. Sci. 30 (2016)","DOI":"10.1016\/j.jksuci.2016.11.001"},{"key":"2554_CR5","doi-asserted-by":"publisher","first-page":"2135","DOI":"10.1109\/TII.2014.2359416","volume":"10","author":"X Bai","year":"2014","unstructured":"Bai, X., Fang, Y., Lin, W., Wang, L., Ju, B.F.: Saliency-based defect detection in industrial images by using phase spectrum. IEEE Trans. Ind. Inform. 10, 2135\u20132145 (2014)","journal-title":"IEEE Trans. Ind. Inform."},{"key":"2554_CR6","doi-asserted-by":"crossref","unstructured":"Borwankar, R., Ludwig, R.: An optical surface inspection and automatic classification technique using the rotated wavelet transform. IEEE Trans. Instrum. Measurement, pp. 1\u20138 (2018)","DOI":"10.1109\/TIM.2017.2783098"},{"issue":"14","key":"2554_CR7","doi-asserted-by":"publisher","first-page":"1331","DOI":"10.1016\/j.ijleo.2015.04.017","volume":"126","author":"G-H Hu","year":"2015","unstructured":"Hu, G.-H.: Automated defect detection in textured surfaces using optimal elliptical gabor filters. Optik - Int. J. Light Electron Opt. 126(14), 1331\u20131340 (2015)","journal-title":"Optik - Int. J. Light Electron Opt."},{"key":"2554_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10845-018-1415-x","volume":"30","author":"H Lin","year":"2019","unstructured":"Lin, H., Li, B., Wang, X., Shu, Y., Niu, S.: Automated defect inspection of led chip using deep convolutional neural network. J. Intell. Manuf. 30, 1\u201310 (2019)","journal-title":"J. Intell. Manuf."},{"key":"2554_CR9","doi-asserted-by":"crossref","unstructured":"Kim, J., Ko, J., Choi, H., Kim, H.: Printed circuit board defect detection using deep learning via a skip-connected convolutional autoencoder. Sensors (Basel, Switzerland) 21 (2021)","DOI":"10.3390\/s21154968"},{"key":"2554_CR10","doi-asserted-by":"crossref","unstructured":"Liu, Z., Liu, S., Li, C., Ding, S., Dong, Y.: Fabric defects detection based on ssd (2018)","DOI":"10.1145\/3282286.3282300"},{"key":"2554_CR11","doi-asserted-by":"publisher","first-page":"1836","DOI":"10.1109\/TASE.2019.2900170","volume":"16","author":"Q Xie","year":"2019","unstructured":"Xie, Q., Li, D., Xu, J., Yu, Z., Wang, J.: Automatic detection and classification of sewer defects via hierarchical deep learning. IEEE Trans. Autom. Sci. Eng. 16, 1836\u20131847 (2019)","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"2554_CR12","doi-asserted-by":"crossref","unstructured":"Lin, W.Y., Lin, C.Y., Chen, G., Hsu, C.Y.: Steel surface defects detection based on deep learning. In: Advances in physical ergonomics and human factors (2018)","DOI":"10.1007\/978-3-319-94484-5_15"},{"key":"2554_CR13","doi-asserted-by":"publisher","first-page":"190663","DOI":"10.1109\/ACCESS.2020.3032108","volume":"8","author":"Y Qiu","year":"2020","unstructured":"Qiu, Y., Tang, L., Li, B., Niu, S., Niu, T.: Uneven illumination surface defects inspection based on saliency detection and intrinsic image decomposition. IEEE Access 8, 190663\u2013190676 (2020)","journal-title":"IEEE Access"},{"key":"2554_CR14","doi-asserted-by":"crossref","unstructured":"Feng, X., Li, J., Hua, Z., Zhang, F.: Low-light image enhancement based on multi-illumination estimation. Appl. Intell. 51, 1\u201321","DOI":"10.1007\/s10489-020-02119-y"},{"key":"2554_CR15","doi-asserted-by":"crossref","unstructured":"Protopapadakis, E., Voulodimos, A., Doulamis, A., Doulamis, N., Stathaki, T.: Automatic crack detection for tunnel inspection using deep learning and heuristic image post-processing. Appl. Intell. 49 (2019)","DOI":"10.1007\/s10489-018-01396-y"},{"issue":"99","key":"2554_CR16","doi-asserted-by":"publisher","first-page":"152161","DOI":"10.1109\/ACCESS.2020.3017691","volume":"8","author":"X Du","year":"2020","unstructured":"Du, X., Cheng, Y., Gu, Z.: Change detection: the framework of visual inspection system for railway plug defects. IEEE Access 8(99), 152161\u2013152172 (2020)","journal-title":"IEEE Access"},{"key":"2554_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2020.103438","volume":"121","author":"M Wang","year":"2021","unstructured":"Wang, M., Kumar, S.S., Cheng, J.: Automated sewer pipe defect tracking in cctv videos based on defect detection and metric learning. Autom. Constr. 121, 103438 (2021)","journal-title":"Autom. Constr."},{"key":"2554_CR18","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1016\/j.ins.2019.10.032","volume":"512","author":"L Jia","year":"2020","unstructured":"Jia, L., Chen, C., Xu, S., Shen, J.: Fabric defect inspection based on lattice segmentation and template statistics. Inf. Sci. 512, 964\u2013984 (2020)","journal-title":"Inf. Sci."},{"key":"2554_CR19","doi-asserted-by":"crossref","unstructured":"Hu, W., Wang, T., Wang, Y., Chen, Z.Y., Huang, G.: Le-msfe-ddnet: a defect detection network based on low-light enhancement and multi-scale feature extraction. Vis. Comput., pp. 1\u201315 (2021)","DOI":"10.1007\/s00371-021-02210-6"},{"key":"2554_CR20","doi-asserted-by":"publisher","first-page":"720","DOI":"10.4236\/opj.2013.32B025","volume":"03","author":"H Wang","year":"2013","unstructured":"Wang, H., Chen, Z., Sun, L.: Image preprocessing methods to identify micro-cracks of road pavement. Opt. Photonics J. 03, 720\u2013726 (2013)","journal-title":"Opt. Photonics J."},{"key":"2554_CR21","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1111\/cote.12239","volume":"133","author":"J Jing","year":"2017","unstructured":"Jing, J., Chen, S.B., Li, P.: Fabric defect detection based on golden image subtraction. Color. Technol. 133, 26\u201339 (2017)","journal-title":"Color. Technol."},{"key":"2554_CR22","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1109\/LSP.2018.2816569","volume":"25","author":"B Li","year":"2018","unstructured":"Li, B., Wei, W., Ferreira, A., Tan, S.: Rest-net: diverse activation modules and parallel subnets-based cnn for spatial image steganalysis. IEEE Signal Process. Lett. 25, 650\u2013654 (2018)","journal-title":"IEEE Signal Process. Lett."},{"issue":"3","key":"2554_CR23","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s00371-015-1065-3","volume":"32","author":"W Zhang","year":"2016","unstructured":"Zhang, W., Xiong, Q., Shi, W., Chen, S.: Region saliency detection via multi-feature on absorbing markov chain. Vis. Comput. 32(3), 275\u2013287 (2016)","journal-title":"Vis. Comput."},{"key":"2554_CR24","doi-asserted-by":"publisher","first-page":"118164","DOI":"10.1109\/ACCESS.2020.3005152","volume":"8","author":"K Shankar","year":"2020","unstructured":"Shankar, K., Zhang, Y., Liu, Y., Wu, L., Chen, C.H.: Hyperparameter tuning deep learning for diabetic retinopathy fundus image classification. IEEE Access 8, 118164\u2013118173 (2020)","journal-title":"IEEE Access"},{"issue":"18","key":"2554_CR25","first-page":"3681","volume":"118","author":"S Perumal","year":"2018","unstructured":"Perumal, S., Velmurugan, T.: Preprocessing by contrast enhancement techniques for medical images. Int. J. Pure Appl. Math. 118(18), 3681\u20133688 (2018)","journal-title":"Int. J. Pure Appl. Math."},{"key":"2554_CR26","doi-asserted-by":"crossref","unstructured":"Chen, S., Li, X.: On image preprocessing methods for underwater image matching. In: 2021 International Conference on Computer Communication and Artificial Intelligence (CCAI), pp. 36\u201340 (2021)","DOI":"10.1109\/CCAI50917.2021.9447538"},{"key":"2554_CR27","unstructured":"Amiri, S.A., Hassanpour, H.: A preprocessing approach for image analysis using gamma correction. Int. J. Comput. Appl. 38 (2012)"},{"key":"2554_CR28","doi-asserted-by":"crossref","unstructured":"Haddad, B.M., Karam, L., Ye, J., Patel, N.S., Oberk\u00f6nig, M.: Multi-feature sparse-based defect detection and classification in semiconductor units. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 754\u2013758 (2016)","DOI":"10.1109\/ICIP.2016.7532458"},{"key":"2554_CR29","doi-asserted-by":"publisher","first-page":"1897","DOI":"10.1007\/s00371-019-01779-3","volume":"36","author":"B Wang","year":"2019","unstructured":"Wang, B., Chen, S., Wang, J., Hu, X.: Residual feature pyramid networks for salient object detection. Vis. Comput. 36, 1897\u20131908 (2019)","journal-title":"Vis. Comput."},{"issue":"99","key":"2554_CR30","first-page":"1493","volume":"69","author":"Y He","year":"2019","unstructured":"He, Y., 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(99), 1493\u20131504 (2019)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"2554_CR31","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."},{"key":"2554_CR32","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.neunet.2020.06.019","volume":"130","author":"B Wei","year":"2020","unstructured":"Wei, B., He, H., Hao, K., Gao, L., Tang, X.S.: Visual interaction networks: a novel bio-inspired computational model for image classification. Neural Netw. 130, 100\u2013110 (2020)","journal-title":"Neural Netw."},{"key":"2554_CR33","doi-asserted-by":"publisher","first-page":"2368","DOI":"10.1109\/TIP.2017.2787612","volume":"27","author":"W Wang","year":"2018","unstructured":"Wang, W., Shen, J.: Deep visual attention prediction. IEEE Trans. Image Process. 27, 2368\u20132378 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"2554_CR34","first-page":"1","volume":"70","author":"X Cheng","year":"2021","unstructured":"Cheng, X., Yu, J.: Retinanet with difference channel attention and adaptively spatial feature fusion for steel surface defect detection. IEEE Trans. Instrum. Meas. 70, 1\u201311 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"2554_CR35","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"2554_CR36","doi-asserted-by":"publisher","first-page":"34627","DOI":"10.1007\/s11042-019-08042-w","volume":"78","author":"S Wu","year":"2019","unstructured":"Wu, S., Wu, Y., Cao, D., Zheng, C.: A fast button surface defect detection method based on Siamese network with imbalanced samples. Multimedia Tools Appl. 78, 34627\u201334648 (2019)","journal-title":"Multimedia Tools Appl."},{"key":"2554_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10489-018-1377-x","volume":"49","author":"S Lu","year":"2019","unstructured":"Lu, S., Wang, H., Zhou, Z.: All-in-one multicategory ramp loss maximum margin of twin spheres support vector machine. Appl. Intell. 49, 1\u201314 (2019)","journal-title":"Appl. Intell."},{"key":"2554_CR38","unstructured":"Sohn, K.: Improved Deep Metric Learning with Multi-class n-pair Loss Objective. Curran Associates Inc (2016)"},{"key":"2554_CR39","doi-asserted-by":"crossref","unstructured":"Sun, S.j.: Self-attention enhanced cnns with average margin loss for chinese zero pronoun resolution. Appl. Intell. (2021)","DOI":"10.1007\/s10489-021-02697-5"},{"key":"2554_CR40","doi-asserted-by":"crossref","unstructured":"Zhao, D., Chen, C., Li, D.: Multi-stage attention and center triplet loss for person re-identication. Appl. Intell., pp. 1\u201313 (2021)","DOI":"10.1007\/s10489-021-02511-2"},{"key":"2554_CR41","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"TY Lin","year":"2020","unstructured":"Lin, T.Y., Goyal, P., Girshick, R.B., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42, 318\u2013327 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2554_CR42","doi-asserted-by":"crossref","unstructured":"Wang, N., Zhao, X., Jiang, Y., Gao, Y.: Iterative metric learning for imbalance data classification (2018)","DOI":"10.24963\/ijcai.2018\/389"},{"key":"2554_CR43","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: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936\u2013944 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"2554_CR44","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2554_CR45","unstructured":"Park, T., Zhu, J.Y., Wang, O., Lu, J., Shechtman, E., Efros, A.A., Zhang, R.: Swapping autoencoder for deep image manipulation. arXiv:2007.00653 (2020)"},{"key":"2554_CR46","unstructured":"Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv:1804.02767 (2018)"},{"issue":"6","key":"2554_CR47","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":"2554_CR48","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9626\u20139635 (2019)","DOI":"10.1109\/ICCV.2019.00972"},{"key":"2554_CR49","doi-asserted-by":"crossref","unstructured":"Feng, Z., Guo, L., Huang, D., Li, R.: Electrical insulator defects detection method based on yolov5. In: 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS), pp. 979\u2013984 (2021)","DOI":"10.1109\/DDCLS52934.2021.9455519"},{"key":"2554_CR50","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.: Ssd: single shot multibox detector, pp. 21\u201337 (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"2554_CR51","doi-asserted-by":"publisher","unstructured":"Luan, C., Cui, R., Sun, L., Lin, Z.: A siamese network utilizing image structural differences for cross-category defect detection. pp. 778\u2013782 (2020). https:\/\/doi.org\/10.1109\/ICIP40778.2020.9191128","DOI":"10.1109\/ICIP40778.2020.9191128"},{"key":"2554_CR52","doi-asserted-by":"crossref","unstructured":"Liu, Z., Wang, B., Li, C., Li, B., Liu, X.: Fabric defect detection algorithm based on convolution neural network and low-rank representation. In: 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR), pp. 465\u2013470 (2017)","DOI":"10.1109\/ACPR.2017.34"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-022-02554-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-022-02554-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-022-02554-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T15:07:17Z","timestamp":1693580837000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-022-02554-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,17]]},"references-count":52,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["2554"],"URL":"https:\/\/doi.org\/10.1007\/s00371-022-02554-7","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"type":"print","value":"0178-2789"},{"type":"electronic","value":"1432-2315"}],"subject":[],"published":{"date-parts":[[2022,6,17]]},"assertion":[{"value":"25 May 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}