{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T16:41:43Z","timestamp":1764002503428,"version":"3.37.3"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T00:00:00Z","timestamp":1649116800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T00:00:00Z","timestamp":1649116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Key Areas Research and Development Program of Guangdong Province","award":["2018B010109007"],"award-info":[{"award-number":["2018B010109007"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s00521-022-07192-7","type":"journal-article","created":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T18:02:22Z","timestamp":1649181742000},"page":"13697-13712","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An axially decomposed self-attention network for the precise segmentation of surface defects on printed circuit boards"],"prefix":"10.1007","volume":"34","author":[{"given":"Danqing","family":"Kang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3883-2024","authenticated-orcid":false,"given":"Jianhuang","family":"Lai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,5]]},"reference":[{"issue":"2","key":"7192_CR1","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/s00521-018-3911-5","volume":"32","author":"AAAM Amiruddin","year":"2020","unstructured":"Amiruddin AAAM, Zabiri H, Taqvi SAA et al (2020) Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems. Neural Comput Appl 32(2):447\u2013472. https:\/\/doi.org\/10.1007\/s00521-018-3911-5","journal-title":"Neural Comput Appl"},{"issue":"2","key":"7192_CR2","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.engappai.2008.05.006","volume":"22","author":"H Bu","year":"2009","unstructured":"Bu H, Wang J, Huang X (2009) Fabric defect detection based on multiple fractal features and support vector data description. Eng Appl Artif Intell 22(2):224\u2013235. https:\/\/doi.org\/10.1016\/j.engappai.2008.05.006","journal-title":"Eng Appl Artif Intell"},{"key":"7192_CR3","doi-asserted-by":"publisher","unstructured":"Buades A, Coll B, Morel J (2005) A non-local algorithm for image denoising. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR 2005), 20-26 June 2005, San Diego, CA, USA. IEEE Computer Society, pp 60\u201365, https:\/\/doi.org\/10.1109\/CVPR.2005.38","DOI":"10.1109\/CVPR.2005.38"},{"key":"7192_CR4","doi-asserted-by":"publisher","unstructured":"Cao Y, Xu J, Lin S, et al (2019) GCNet: Non-local networks meet squeeze-excitation networks and beyond. In: 2019 IEEE\/CVF international conference on computer vision workshops, ICCV workshops 2019, Seoul, Korea (South), October 27-28, 2019. IEEE, pp 1971\u20131980, https:\/\/doi.org\/10.1109\/ICCVW.2019.00246","DOI":"10.1109\/ICCVW.2019.00246"},{"issue":"15","key":"7192_CR5","doi-asserted-by":"publisher","first-page":"11,229","DOI":"10.1007\/s00521-020-04819-5","volume":"32","author":"H Chen","year":"2020","unstructured":"Chen H, Hu Q, Zhai B et al (2020) A robust weakly supervised learning of deep conv-nets for surface defect inspection. Neural Comput Appl 32(15):11,229-11,244. https:\/\/doi.org\/10.1007\/s00521-020-04819-5","journal-title":"Neural Comput Appl"},{"issue":"4","key":"7192_CR6","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L Chen","year":"2018","unstructured":"Chen L, Papandreou G, Kokkinos I et al (2018) DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848. https:\/\/doi.org\/10.1109\/TPAMI.2017.2699184","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7192_CR7","doi-asserted-by":"publisher","unstructured":"Chen L, Zhu Y, Papandreou G, et al (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part VII, Lecture Notes in Computer Science, vol 11211. Springer, pp 833\u2013851, https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"7192_CR8","unstructured":"Chen LC, Papandreou G, Schroff F, et al (2017) Rethinking atrous convolution for semantic image segmentation. CoRR arXiv:1706.05587"},{"key":"7192_CR9","doi-asserted-by":"publisher","unstructured":"Deng J, Dong W, Socher R, et al (2009) ImageNet: A large-scale hierarchical image database. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA. IEEE Computer Society, pp 248\u2013255, https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"7192_CR10","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/s10845-020-01566-1","volume":"32","author":"W Du","year":"2021","unstructured":"Du W, Shen H, Fu J et al (2021) Automated detection of defects with low semantic information in X-ray images based on deep learning. J Intell Manuf 32:141\u2013156. https:\/\/doi.org\/10.1007\/s10845-020-01566-1","journal-title":"J Intell Manuf"},{"key":"7192_CR11","doi-asserted-by":"publisher","unstructured":"Fu J, Liu J, Tian H, et al (2019) Dual attention network for scene segmentation. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR). Computer vision foundation \/ IEEE, pp 3141\u20133149, https:\/\/doi.org\/10.1109\/CVPR.2019.00326","DOI":"10.1109\/CVPR.2019.00326"},{"key":"7192_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/J.JMSY.2021.05.008","volume":"1","author":"Y Gao","year":"2021","unstructured":"Gao Y, Li X, Wang XV et al (2021) A review on recent advances in vision-based defect recognition towards industrial intelligence. J Manuf Syst 1:1\u20131. https:\/\/doi.org\/10.1016\/J.JMSY.2021.05.008","journal-title":"J Manuf Syst"},{"key":"7192_CR13","doi-asserted-by":"publisher","unstructured":"Girshick RB (2015) Fast R-CNN. In: 2015 IEEE International conference on computer vision, ICCV 2015, Santiago, Chile, December 7-13, 2015. IEEE Computer Society, pp 1440\u20131448, https:\/\/doi.org\/10.1109\/ICCV.2015.169","DOI":"10.1109\/ICCV.2015.169"},{"key":"7192_CR14","doi-asserted-by":"publisher","unstructured":"Gryllias K, Antoniadis I (2012) A support vector machine approach based on physical model training for rolling element bearing fault detection in industrial environments. Engineering Applications of Artificial Intelligence 25(2):326\u2013344. https:\/\/doi.org\/10.1016\/j.engappai.2011.09.010, special Section: Local Search Algorithms for Real-World Scheduling and Planning","DOI":"10.1016\/j.engappai.2011.09.010"},{"key":"7192_CR15","doi-asserted-by":"publisher","unstructured":"He J, Deng Z, Qiao Y (2019) Dynamic multi-scale filters for semantic segmentation. In: 2019 IEEE\/CVF international conference on computer vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019. IEEE, pp 3561\u20133571, https:\/\/doi.org\/10.1109\/ICCV.2019.00366","DOI":"10.1109\/ICCV.2019.00366"},{"key":"7192_CR16","doi-asserted-by":"publisher","unstructured":"He J, Deng Z, Zhou L, et al (2019) Adaptive pyramid context network for semantic segmentation. In: IEEE conference on computer vision and pattern recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019. Computer Vision Foundation \/ IEEE, pp 7519\u20137528, https:\/\/doi.org\/10.1109\/CVPR.2019.00770","DOI":"10.1109\/CVPR.2019.00770"},{"key":"7192_CR17","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, et al (2014) Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Computer Vision - ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III, Lecture Notes in Computer Science, vol 8691. Springer, pp 346\u2013361, https:\/\/doi.org\/10.1007\/978-3-319-10578-9\/23","DOI":"10.1007\/978-3-319-10578-9\/23"},{"key":"7192_CR18","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, et al (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016. IEEE Computer Society, pp 770\u2013778, https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"7192_CR19","doi-asserted-by":"publisher","unstructured":"He T, Zhang Z, Zhang H, et al (2019) Bag of tricks for image classification with convolutional neural networks. In: IEEE conference on computer vision and pattern recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019. Computer Vision Foundation \/ IEEE, pp 558\u2013567, https:\/\/doi.org\/10.1109\/CVPR.2019.00065","DOI":"10.1109\/CVPR.2019.00065"},{"key":"7192_CR20","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1109\/TPAMI.2020.3007032","volume":"1","author":"Z Huang","year":"2020","unstructured":"Huang Z, Wang X, Wei Y et al (2020) CCNet: Criss-cross attention for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 1:603\u2013612. https:\/\/doi.org\/10.1109\/TPAMI.2020.3007032","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"15","key":"7192_CR21","doi-asserted-by":"publisher","first-page":"9289","DOI":"10.1007\/s00521-021-05690-8","volume":"33","author":"B Kim","year":"2021","unstructured":"Kim B, Yuvaraj N, SriPreethaa KR et al (2021) Surface crack detection using deep learning with shallow CNN architecture for enhanced computation. Neural Comput Appl 33(15):9289\u20139305. https:\/\/doi.org\/10.1007\/s00521-021-05690-8","journal-title":"Neural Comput Appl"},{"key":"7192_CR22","doi-asserted-by":"publisher","unstructured":"Kirillov A, Wu Y, He K, et al (2020) PointRend: Image segmentation as rendering. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020. IEEE, pp 9796\u20139805, https:\/\/doi.org\/10.1109\/CVPR42600.2020.00982","DOI":"10.1109\/CVPR42600.2020.00982"},{"issue":"4","key":"7192_CR23","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun Y, Boser BE, Denker JS et al (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541\u2013551. https:\/\/doi.org\/10.1162\/neco.1989.1.4.541","journal-title":"Neural Comput"},{"key":"7192_CR24","doi-asserted-by":"publisher","unstructured":"Li X, Zhong Z, Wu J, et al (2019) Expectation-maximization attention networks for semantic segmentation. In: 2019 IEEE\/CVF international conference on computer vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019. IEEE, pp 9166\u20139175, https:\/\/doi.org\/10.1109\/ICCV.2019.00926","DOI":"10.1109\/ICCV.2019.00926"},{"issue":"3","key":"7192_CR25","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1109\/TIM.2019.2963555","volume":"69","author":"Q Luo","year":"2020","unstructured":"Luo Q, Fang X, Liu L et al (2020) Automated visual defect detection for flat steel surface: A survey. IEEE Trans Instrum Meas 69(3):626\u2013644. https:\/\/doi.org\/10.1109\/TIM.2019.2963555","journal-title":"IEEE Trans Instrum Meas"},{"key":"7192_CR26","doi-asserted-by":"publisher","first-page":"1545","DOI":"10.1109\/LSP.2020.3020227","volume":"27","author":"W Luo","year":"2020","unstructured":"Luo W, Zhang H, Li J et al (2020) Learning semantically enhanced feature for fine-grained image classification. IEEE Signal Process Lett 27:1545\u20131549. https:\/\/doi.org\/10.1109\/LSP.2020.3020227","journal-title":"IEEE Signal Process Lett"},{"key":"7192_CR27","doi-asserted-by":"publisher","unstructured":"Maniat M, Camp CV, Kashani A (2021) Deep learning-based visual crack detection using google street view images. Neural Computing and Applications pp 1\u201318. https:\/\/doi.org\/10.1007\/S00521-021-06098-0","DOI":"10.1007\/S00521-021-06098-0"},{"issue":"107","key":"7192_CR28","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1016\/j.measurement.2020.107722","volume":"158","author":"W Ming","year":"2020","unstructured":"Ming W, Shen F, Li X et al (2020) A comprehensive review of defect detection in 3C glass components. Measurement 158(107):722. https:\/\/doi.org\/10.1016\/j.measurement.2020.107722","journal-title":"Measurement"},{"issue":"3","key":"7192_CR29","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1016\/j.engappai.2012.03.016","volume":"26","author":"MPL Ooi","year":"2013","unstructured":"Ooi MPL, Sok HK, Kuang YC et al (2013) Defect cluster recognition system for fabricated semiconductor wafers. Eng Appl Artif Intell 26(3):1029\u20131043. https:\/\/doi.org\/10.1016\/j.engappai.2012.03.016","journal-title":"Eng Appl Artif Intell"},{"key":"7192_CR30","unstructured":"Santoro A, Raposo D, Barrett DGT, et al (2017) A simple neural network module for relational reasoning. In: Advances in neural information processing systems 30: Annual conference on neural information processing systems 2017, December 4-9, 2017, Long Beach, CA, USA, pp 4967\u20134976"},{"issue":"4","key":"7192_CR31","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640\u2013651. https:\/\/doi.org\/10.1109\/TPAMI.2016.2572683","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7192_CR32","doi-asserted-by":"publisher","unstructured":"Shrivastava A, Gupta A, Girshick RB (2016) Training region-based object detectors with online hard example mining. In: 2016 IEEE conference on computer vision and pattern recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016. IEEE Computer Society, pp 761\u2013769, https:\/\/doi.org\/10.1109\/CVPR.2016.89","DOI":"10.1109\/CVPR.2016.89"},{"issue":"107","key":"7192_CR33","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.measurement.2019.107357","volume":"152","author":"H Ting","year":"2020","unstructured":"Ting H, Liu Y, Yabin Y et al (2020) Application of deep convolutional neural network on feature extraction and detection of wood defects. Measurement 152(107):357. https:\/\/doi.org\/10.1016\/j.measurement.2019.107357","journal-title":"Measurement"},{"key":"7192_CR34","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.measurement.2018.10.079","volume":"134","author":"DM Tsai","year":"2019","unstructured":"Tsai DM, Rivera Molina DE (2019) Morphology-based defect detection in machined surfaces with circular tool-mark patterns. Measurement 134:209\u2013217. https:\/\/doi.org\/10.1016\/j.measurement.2018.10.079","journal-title":"Measurement"},{"key":"7192_CR35","unstructured":"Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. In: Advances in neural information processing systems 30: annual conference on neural information processing systems 2017, December 4-9, 2017, Long Beach, CA, USA, pp 5998\u20136008"},{"key":"7192_CR36","doi-asserted-by":"publisher","unstructured":"Wang X, Girshick RB, Gupta A, et al (2018) Non-local neural networks. In: 2018 IEEE conference on computer vision and pattern recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018. IEEE Computer Society, pp 7794\u20137803, https:\/\/doi.org\/10.1109\/CVPR.2018.00813","DOI":"10.1109\/CVPR.2018.00813"},{"issue":"106","key":"7192_CR37","doi-asserted-by":"publisher","first-page":"886","DOI":"10.1016\/J.MEASUREMENT.2019.106886","volume":"147","author":"Z Wang","year":"2019","unstructured":"Wang Z, Zhu D (2019) An accurate detection method for surface defects of complex components based on support vector machine and spreading algorithm. Measurement 147(106):886. https:\/\/doi.org\/10.1016\/J.MEASUREMENT.2019.106886","journal-title":"Measurement"},{"key":"7192_CR38","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.engappai.2019.01.008","volume":"80","author":"X Wei","year":"2019","unstructured":"Wei X, Yang Z, Liu Y et al (2019) Railway track fastener defect detection based on image processing and deep learning techniques: A comparative study. Eng Appl Artif Intell 80:66\u201381. https:\/\/doi.org\/10.1016\/j.engappai.2019.01.008","journal-title":"Eng Appl Artif Intell"},{"key":"7192_CR39","doi-asserted-by":"publisher","unstructured":"Xiao T, Liu Y, Zhou B, et al (2018) Unified perceptual parsing for scene understanding. In: Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part V, Lecture Notes in Computer Science, vol 11209. Springer, pp 432\u2013448, https:\/\/doi.org\/10.1007\/978-3-030-01228-1\/26","DOI":"10.1007\/978-3-030-01228-1\/26"},{"issue":"109","key":"7192_CR40","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/J.MEASUREMENT.2021.109185","volume":"176","author":"J Xing","year":"2021","unstructured":"Xing J, Jia M (2021) A convolutional neural network-based method for workpiece surface defect detection. Measurement 176(109):185. https:\/\/doi.org\/10.1016\/J.MEASUREMENT.2021.109185","journal-title":"Measurement"},{"key":"7192_CR41","doi-asserted-by":"publisher","unstructured":"Yin M, Yao Z, Cao Y, et al (2020) Disentangled non-local neural networks. In: Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XV, Lecture Notes in Computer Science, vol 12360. Springer, pp 191\u2013207, https:\/\/doi.org\/10.1007\/978-3-030-58555-6\/12","DOI":"10.1007\/978-3-030-58555-6\/12"},{"key":"7192_CR42","doi-asserted-by":"publisher","unstructured":"Yu C, Wang J, Peng C, et al (2018) Learning a discriminative feature network for semantic segmentation. In: 2018 IEEE conference on computer vision and pattern recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018. IEEE Computer Society, pp 1857\u20131866, https:\/\/doi.org\/10.1109\/CVPR.2018.00199","DOI":"10.1109\/CVPR.2018.00199"},{"key":"7192_CR43","doi-asserted-by":"publisher","unstructured":"Yuan Y, Chen X, Wang J (2020) Object-contextual representations for semantic segmentation. In: Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part VI, Lecture Notes in Computer Science, vol 12351. Springer, pp 173\u2013190, https:\/\/doi.org\/10.1007\/978-3-030-58539-6\/11","DOI":"10.1007\/978-3-030-58539-6\/11"},{"key":"7192_CR44","doi-asserted-by":"publisher","unstructured":"Zhang H, Dana KJ, Shi J, et al (2018) Context encoding for semantic segmentation. In: 2018 IEEE conference on computer vision and pattern recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018. IEEE Computer Society, pp 7151\u20137160, https:\/\/doi.org\/10.1109\/CVPR.2018.00747","DOI":"10.1109\/CVPR.2018.00747"},{"issue":"1","key":"7192_CR45","doi-asserted-by":"publisher","first-page":"115673","DOI":"10.1016\/j.eswa.2021.115673","volume":"185","author":"H Zhang","year":"2021","unstructured":"Zhang H, Jiang L, Li C (2021) CS-ResNet: Cost-sensitive residual convolutional neural network for PCB cosmetic defect detection. Expert Syst Appl 185(1):115673. https:\/\/doi.org\/10.1016\/j.eswa.2021.115673","journal-title":"Expert Syst Appl"},{"issue":"104","key":"7192_CR46","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.engappai.2021.104242","volume":"102","author":"K Zhang","year":"2021","unstructured":"Zhang K, Wang W, Lv Z et al (2021) Computer vision detection of foreign objects in coal processing using attention CNN. Eng Appl Artif Intell 102(104):242. https:\/\/doi.org\/10.1016\/j.engappai.2021.104242","journal-title":"Eng Appl Artif Intell"},{"key":"7192_CR47","doi-asserted-by":"publisher","unstructured":"Zhao H, Shi J, Qi X, et al (2017) Pyramid scene parsing network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017. IEEE Computer Society, pp 6230\u20136239, https:\/\/doi.org\/10.1109\/CVPR.2017.660","DOI":"10.1109\/CVPR.2017.660"},{"key":"7192_CR48","doi-asserted-by":"publisher","unstructured":"Zhao H, Zhang Y, Liu S, et al (2018) PSANet: Point-wise spatial attention network for scene parsing. In: Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part IX, Lecture Notes in Computer Science, vol 11213. Springer, pp 270\u2013286, https:\/\/doi.org\/10.1007\/978-3-030-01240-3\/17","DOI":"10.1007\/978-3-030-01240-3\/17"},{"issue":"108","key":"7192_CR49","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1016\/j.measurement.2020.108885","volume":"172","author":"T Zhou","year":"2021","unstructured":"Zhou T, Zhang J, Su H et al (2021) EDDs: a series of efficient defect detectors for fabric quality inspection. Measurement 172(108):885. https:\/\/doi.org\/10.1016\/j.measurement.2020.108885","journal-title":"Measurement"},{"key":"7192_CR50","doi-asserted-by":"publisher","unstructured":"Zhu Z, Xu M, Bai S, et al (2019) Asymmetric non-local neural networks for semantic segmentation. In: 2019 IEEE\/CVF International conference on computer vision (ICCV). IEEE, pp 593\u2013602, https:\/\/doi.org\/10.1109\/ICCV.2019.00068","DOI":"10.1109\/ICCV.2019.00068"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07192-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07192-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07192-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,24]],"date-time":"2022-07-24T10:09:54Z","timestamp":1658657394000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07192-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,5]]},"references-count":50,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["7192"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07192-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,4,5]]},"assertion":[{"value":"27 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"These have no potential competing interests in this paper. We confirm that the content of the manuscript has not been published or submitted for publication elsewhere. All authors have seen the manuscript and approved to submit to your journal.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}