{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T18:58:00Z","timestamp":1774292280233,"version":"3.50.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T00:00:00Z","timestamp":1692576000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T00:00:00Z","timestamp":1692576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s00371-023-03056-w","type":"journal-article","created":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T12:02:18Z","timestamp":1692619338000},"page":"3633-3648","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Feature reused network: a fast segmentation network model for strip steel surfaces defects based on feature reused"],"prefix":"10.1007","volume":"40","author":[{"given":"Qiang","family":"Feng","sequence":"first","affiliation":[]},{"given":"Fang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Hua","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiaodong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jiyou","family":"Fei","sequence":"additional","affiliation":[]},{"given":"Shuai","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Chang","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,21]]},"reference":[{"issue":"7","key":"3056_CR1","doi-asserted-by":"publisher","first-page":"1833","DOI":"10.1007\/s10845-020-01670-2","volume":"32","author":"R Hao","year":"2020","unstructured":"Hao, R., Lu, B., Cheng, Y., Li, X., Huang, B.: A steel surface defect inspection approach towards smart industrial monitoring. J. Intell. Manuf. 32(7), 1833\u20131843 (2020). https:\/\/doi.org\/10.1007\/s10845-020-01670-2","journal-title":"J. Intell. Manuf."},{"key":"3056_CR2","doi-asserted-by":"publisher","first-page":"149465","DOI":"10.1109\/access.2021.3124814","volume":"9","author":"X Zhou","year":"2021","unstructured":"Zhou, X., Fang, H., Fei, X., Shi, R., Zhang, J.: Edge-aware multi-level interactive network for salient object detection of strip steel surface defects. IEEE Access 9, 149465\u2013149476 (2021). https:\/\/doi.org\/10.1109\/access.2021.3124814","journal-title":"IEEE Access"},{"key":"3056_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tim.2021.3132082","volume":"71","author":"X Zhou","year":"2022","unstructured":"Zhou, X., Fang, H., Liu, Z., Zheng, B., Sun, Y., Zhang, J., Yan, C.: Dense attention-guided cascaded network for salient object detection of strip steel surface defects. IEEE Trans. Instrum. Meas. 71, 1\u201314 (2022). https:\/\/doi.org\/10.1109\/tim.2021.3132082","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"12","key":"3056_CR4","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). https:\/\/doi.org\/10.1109\/tim.2020.3002277","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"3056_CR5","doi-asserted-by":"publisher","first-page":"9245449","DOI":"10.3390\/app9245449","volume":"9","author":"SY Lee","year":"2019","unstructured":"Lee, S.Y., Tama, B.A., Moon, S.J., Lee, S.: Steel surface defect diagnostics using deep convolutional neural network and class activation map. Appl. Sci. 9, 9245449 (2019). https:\/\/doi.org\/10.3390\/app9245449","journal-title":"Appl. Sci."},{"key":"3056_CR6","doi-asserted-by":"publisher","first-page":"130271","DOI":"10.1016\/j.matlet.2021.130271","volume":"301","author":"Z Huang","year":"2021","unstructured":"Huang, Z., Wu, J., Xie, F.: Automatic surface defect segmentation for hot-rolled steel strip using depth-wise separable U-shape network. Mater. Lett. 301, 130271 (2021). https:\/\/doi.org\/10.1016\/j.matlet.2021.130271","journal-title":"Mater. Lett."},{"key":"3056_CR7","doi-asserted-by":"publisher","first-page":"103231","DOI":"10.1016\/j.compind.2020.103231","volume":"122","author":"J Zhang","year":"2020","unstructured":"Zhang, J., Wang, H., Tian, Y., Liu, K.: An accurate fuzzy measure-based detection method for various types of defects on strip steel surfaces. Comput. Ind. 122, 103231 (2020). https:\/\/doi.org\/10.1016\/j.compind.2020.103231","journal-title":"Comput. Ind."},{"key":"3056_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tim.2020.3033726","volume":"70","author":"J Cao","year":"2021","unstructured":"Cao, J., Yang, G., Yang, X.: A pixel-level segmentation convolutional neural network based on deep feature fusion for surface defect detection. IEEE Trans. Instrum. Meas. 70, 1\u201312 (2021). https:\/\/doi.org\/10.1109\/tim.2020.3033726","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"5","key":"3056_CR9","doi-asserted-by":"publisher","first-page":"2431","DOI":"10.1007\/s10845-022-01930-3","volume":"34","author":"Z Ma","year":"2022","unstructured":"Ma, Z., Li, Y., Huang, M., Huang, Q., Cheng, J., Tang, S.: Automated real-time detection of surface defects in manufacturing processes of aluminum alloy strip using a lightweight network architecture. J. Intell. Manuf. 34(5), 2431\u20132447 (2022). https:\/\/doi.org\/10.1007\/s10845-022-01930-3","journal-title":"J. Intell. Manuf."},{"issue":"4","key":"3056_CR10","doi-asserted-by":"publisher","first-page":"1995","DOI":"10.1007\/s10845-021-01878-w","volume":"34","author":"SA Singh","year":"2022","unstructured":"Singh, S.A., Desai, K.A.: Automated surface defect detection framework using machine vision and convolutional neural networks. J. Intell. Manuf. 34(4), 1995\u20132011 (2022). https:\/\/doi.org\/10.1007\/s10845-021-01878-w","journal-title":"J. Intell. Manuf."},{"key":"3056_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tim.2020.3040890","volume":"70","author":"D Zhang","year":"2021","unstructured":"Zhang, D., Song, K., Xu, J., He, Y., Niu, M., Yan, Y.: MCnet: multiple context information segmentation network of no-service rail surface defects. IEEE Trans. Instrum. Meas. 70, 1\u20139 (2021). https:\/\/doi.org\/10.1109\/tim.2020.3040890","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"3056_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-022-02597-w","author":"J Sun","year":"2022","unstructured":"Sun, J., Yan, S., Song, X.: QCNet: query context network for salient object detection of automatic surface inspection. Vis. Comput. (2022). https:\/\/doi.org\/10.1007\/s00371-022-02597-w","journal-title":"Vis. Comput."},{"key":"3056_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tim.2021.3056744","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). https:\/\/doi.org\/10.1109\/tim.2021.3056744","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"3056_CR14","doi-asserted-by":"publisher","first-page":"49885","DOI":"10.1109\/ACCESS.2020.2979755","volume":"8","author":"S Guan","year":"2020","unstructured":"Guan, S., Lei, M., Lu, H.: A steel surface defect recognition algorithm based on improved deep learning network model using feature visualization and quality evaluation. IEEE Access. 8, 49885\u201349895 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2979755","journal-title":"IEEE Access."},{"issue":"6","key":"3056_CR15","doi-asserted-by":"publisher","first-page":"10060846","DOI":"10.3390\/met10060846","volume":"10","author":"I Konovalenko","year":"2020","unstructured":"Konovalenko, I., Maruschak, P., Brezinov\u00e1, J., Vi\u0148\u00e1\u0161, J., Brezina, J.: Steel surface defect classification using deep residual neural network. Metals. 10(6), 10060846 (2020). https:\/\/doi.org\/10.3390\/met10060846","journal-title":"Metals."},{"key":"3056_CR16","doi-asserted-by":"publisher","first-page":"166184","DOI":"10.1109\/access.2020.3022405","volume":"8","author":"W Wu","year":"2020","unstructured":"Wu, W., Li, Q.: Machine vision inspection of electrical connectors based on improved Yolo v3. IEEE Access 8, 166184\u2013166196 (2020). https:\/\/doi.org\/10.1109\/access.2020.3022405","journal-title":"IEEE Access"},{"key":"3056_CR17","doi-asserted-by":"publisher","first-page":"4629","DOI":"10.3390\/ma13204629","volume":"13","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Yuan, Y., Balta, C., Liu, J.: A light-weight deep-learning model with multi-scale features for steel surface defect classification. Materials (Basel) 13, 4629 (2020). https:\/\/doi.org\/10.3390\/ma13204629","journal-title":"Materials (Basel)"},{"key":"3056_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tim.2021.3083561","volume":"70","author":"Y Bao","year":"2021","unstructured":"Bao, Y., Song, K., Liu, J., Wang, Y., Yan, Y., Yu, H., Li, X.: Triplet-graph reasoning network for few-shot metal generic surface defect segmentation. IEEE Trans. Instrum. Meas. 70, 1\u201311 (2021). https:\/\/doi.org\/10.1109\/tim.2021.3083561","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"3056_CR19","doi-asserted-by":"publisher","first-page":"107541","DOI":"10.1016\/j.ymssp.2020.107541","volume":"153","author":"S Zhang","year":"2021","unstructured":"Zhang, S., Zhang, Q., Gu, J., Su, L., Li, K., Pecht, M.: Visual inspection of steel surface defects based on domain adaptation and adaptive convolutional neural network. Mech. Syst. Sign. Proce. 153, 107541 (2021). https:\/\/doi.org\/10.1016\/j.ymssp.2020.107541","journal-title":"Mech. Syst. Sign. Proce."},{"key":"3056_CR20","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.cie.2018.12.043","volume":"128","author":"D He","year":"2019","unstructured":"He, D., Xu, K., Zhou, P.: Defect detection of hot rolled steels with a new object detection framework called classification priority network. Comput. & Ind. Eng. 128, 290\u2013297 (2019). https:\/\/doi.org\/10.1016\/j.cie.2018.12.043","journal-title":"Comput. & Ind. Eng."},{"key":"3056_CR21","doi-asserted-by":"publisher","first-page":"103585","DOI":"10.1016\/j.compind.2021.103585","volume":"136","author":"Z Ma","year":"2022","unstructured":"Ma, Z., Li, Y., Huang, M., Huang, Q., Cheng, J., Tang, S.: A lightweight detector based on attention mechanism for aluminum strip surface defect detection. Comput. Ind. 136, 103585 (2022). https:\/\/doi.org\/10.1016\/j.compind.2021.103585","journal-title":"Comput. Ind."},{"key":"3056_CR22","doi-asserted-by":"publisher","first-page":"110211","DOI":"10.1016\/j.measurement.2021.110211","volume":"187","author":"R Tian","year":"2022","unstructured":"Tian, R., Jia, M.: DCC-CenterNet: a rapid detection method for steel surface defects. Meas. 187, 110211 (2022). https:\/\/doi.org\/10.1016\/j.measurement.2021.110211","journal-title":"Meas."},{"issue":"6","key":"3056_CR23","doi-asserted-by":"publisher","first-page":"61562","DOI":"10.3390\/s20061562","volume":"20","author":"X Lv","year":"2020","unstructured":"Lv, X., Duan, F., Jiang, J.-J., Fu, X., Gan, L.: Deep metallic surface defect detection: the new benchmark and detection network. Sensors. 20(6), 61562 (2020). https:\/\/doi.org\/10.3390\/s20061562","journal-title":"Sensors."},{"issue":"4","key":"3056_CR24","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1109\/tim.2019.2915404","volume":"69","author":"Y He","year":"2020","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(4), 1493\u20131504 (2020). https:\/\/doi.org\/10.1109\/tim.2019.2915404","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"9","key":"3056_CR25","doi-asserted-by":"publisher","first-page":"8016","DOI":"10.1109\/tie.2019.2945265","volume":"67","author":"W Choi","year":"2020","unstructured":"Choi, W., Cha, Y.-J.: SDDNet: real-time crack segmentation. IEEE Trans. Ind. Elect. 67(9), 8016\u20138025 (2020). https:\/\/doi.org\/10.1109\/tie.2019.2945265","journal-title":"IEEE Trans. Ind. Elect."},{"issue":"9","key":"3056_CR26","doi-asserted-by":"publisher","first-page":"7253","DOI":"10.1007\/s00521-021-06792-z","volume":"34","author":"E Antwi-Bekoe","year":"2022","unstructured":"Antwi-Bekoe, E., Liu, G., Ainam, J.-P., Sun, G., Xie, X.: A deep learning approach for insulator instance segmentation and defect detection. Neur. Comput. Appl. 34(9), 7253\u20137269 (2022). https:\/\/doi.org\/10.1007\/s00521-021-06792-z","journal-title":"Neur. Comput. Appl."},{"issue":"16","key":"3056_CR27","doi-asserted-by":"publisher","first-page":"13697","DOI":"10.1007\/s00521-022-07192-7","volume":"34","author":"D Kang","year":"2022","unstructured":"Kang, D., Han, Y., Zhu, J., Lai, J.: An axially decomposed self-attention network for the precise segmentation of surface defects on printed circuit boards. Neur. Comput. Appl. 34(16), 13697\u201313712 (2022). https:\/\/doi.org\/10.1007\/s00521-022-07192-7","journal-title":"Neur. Comput. Appl."},{"key":"3056_CR28","doi-asserted-by":"publisher","first-page":"27547","DOI":"10.1109\/access.2019.2894863","volume":"7","author":"L Song","year":"2019","unstructured":"Song, L., Lin, W., Yang, Y.-G., Zhu, X., Guo, Q., Xi, J.: Weak micro-scratch detection based on deep convolutional neural network. IEEE Access. 7, 27547\u201327554 (2019). https:\/\/doi.org\/10.1109\/access.2019.2894863","journal-title":"IEEE Access."},{"key":"3056_CR29","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.rcim.2015.09.008","volume":"38","author":"Q Luo","year":"2016","unstructured":"Luo, Q., He, Y.: A cost-effective and automatic surface defect inspection system for hot-rolled flat steel. Robot. Comput. Integr. Manuf. 38, 16\u201330 (2016). https:\/\/doi.org\/10.1016\/j.rcim.2015.09.008","journal-title":"Robot. Comput. Integr. Manuf."},{"issue":"24","key":"3056_CR30","doi-asserted-by":"publisher","first-page":"7504","DOI":"10.3390\/ma14247504","volume":"14","author":"P Liu","year":"2021","unstructured":"Liu, P., Song, Y., Chai, M., Han, Z., Zhang, Y.: Swin-UNet++: a nested swin transformer architecture for location identification and morphology segmentation of dimples on 2.25Cr1Mo0.25V fractured surface. Materials (Basel). 14(24), 7504 (2021). https:\/\/doi.org\/10.3390\/ma14247504","journal-title":"Materials (Basel)."},{"key":"3056_CR31","doi-asserted-by":"publisher","first-page":"1745","DOI":"10.1007\/s00371-022-02442-0","volume":"39","author":"H \u00dczen","year":"2022","unstructured":"\u00dczen, H., Turkoglu, M., Aslan, M., Hanbay, D.: Depth-wise squeeze and excitation block-based efficient-unet model for surface defect detection. Visual Comput. 39, 1745\u20131764 (2022). https:\/\/doi.org\/10.1007\/s00371-022-02442-0","journal-title":"Visual Comput."},{"issue":"12","key":"3056_CR32","doi-asserted-by":"publisher","first-page":"7448","DOI":"10.1109\/tii.2019.2958826","volume":"16","author":"H Dong","year":"2020","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. Inf. 16(12), 7448\u20137458 (2020). https:\/\/doi.org\/10.1109\/tii.2019.2958826","journal-title":"IEEE Trans. Ind. Inf."},{"key":"3056_CR33","doi-asserted-by":"publisher","first-page":"106000","DOI":"10.1016\/j.optlaseng.2019.106000","volume":"128","author":"G Song","year":"2020","unstructured":"Song, G., Song, K., Yan, Y.: Saliency detection for strip steel surface defects using multiple constraints and improved texture features. Opt. Las. Eng. 128, 106000 (2020). https:\/\/doi.org\/10.1016\/j.optlaseng.2019.106000","journal-title":"Opt. Las. Eng."},{"key":"3056_CR34","doi-asserted-by":"publisher","first-page":"108698","DOI":"10.1016\/j.measurement.2020.108698","volume":"170","author":"Y Wang","year":"2021","unstructured":"Wang, Y., Song, K., Liu, J., Dong, H., Yan, Y., Jiang, P.: RENet: Rectangular convolution pyramid and edge enhancement network for salient object detection of pavement cracks. Meas. 170, 108698 (2021). https:\/\/doi.org\/10.1016\/j.measurement.2020.108698","journal-title":"Meas."},{"issue":"11","key":"3056_CR35","doi-asserted-by":"publisher","first-page":"1730","DOI":"10.3390\/coatings12111730","volume":"12","author":"C Wan","year":"2022","unstructured":"Wan, C., Ma, S., Song, K.: TSSTNet: a two-stream swin transformer network for salient object detection of no-service rail surface defects. Coatings 12(11), 1730 (2022). https:\/\/doi.org\/10.3390\/coatings12111730","journal-title":"Coatings"},{"issue":"12","key":"3056_CR36","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.1007\/s11265-022-01801-3","volume":"94","author":"J Cao","year":"2022","unstructured":"Cao, J., Yang, G., Yang, X.: TAFFNet: two-stage attention-based feature fusion network for surface defect detection. J. Sign. Pro. Syst. 94(12), 1531\u20131544 (2022). https:\/\/doi.org\/10.1007\/s11265-022-01801-3","journal-title":"J. Sign. Pro. Syst."},{"key":"3056_CR37","doi-asserted-by":"publisher","first-page":"111429","DOI":"10.1016\/j.measurement.2022.111429","volume":"199","author":"T Ding","year":"2022","unstructured":"Ding, T., Li, G., Liu, Z., Wang, Y.: Cross-scale edge purification network for salient object detection of steel defect images. Meas 199, 111429 (2022). https:\/\/doi.org\/10.1016\/j.measurement.2022.111429","journal-title":"Meas"},{"key":"3056_CR38","doi-asserted-by":"publisher","first-page":"106047","DOI":"10.1016\/j.engfailanal.2022.106047","volume":"134","author":"R Mordia","year":"2022","unstructured":"Mordia, R., Kumar, V.A.: Visual techniques for defects detection in steel products: a comparative study. Eng. Failure Anal. 134, 106047 (2022). https:\/\/doi.org\/10.1016\/j.engfailanal.2022.106047","journal-title":"Eng. Failure Anal."},{"issue":"9","key":"3056_CR39","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1007\/s00371-018-1515-9","volume":"35","author":"W Kaddah","year":"2018","unstructured":"Kaddah, W., Elbouz, M., Ouerhani, Y., Baltazart, V., Desthieux, M., Alfalou, A.: Optimized minimal path selection (OMPS) method for automatic and unsupervised crack segmentation within two-dimensional pavement images. Vis. Comput. 35(9), 1293\u20131309 (2018). https:\/\/doi.org\/10.1007\/s00371-018-1515-9","journal-title":"Vis. Comput."},{"key":"3056_CR40","doi-asserted-by":"publisher","unstructured":"Roth K., Pemula L., Zepeda J., Sch\u00f6lkopf B., Brox T., and Gehler P.: Towards Total Recall in Industrial Anomaly Detection. In: Procedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14298\u201314308. IEEE (2022). doi: https:\/\/doi.org\/10.1109\/CVPR52688.2022.01392.","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"3056_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-022-02754-1","author":"C Wei","year":"2022","unstructured":"Wei, C., Liang, J., Liu, H., Hou, Z., Huan, Z.: Multi-stage unsupervised fabric defect detection based on DCGAN. Vis. Comput. (2022). https:\/\/doi.org\/10.1007\/s00371-022-02754-1","journal-title":"Vis. Comput."},{"key":"3056_CR42","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.media.2019.01.010","volume":"54","author":"T Schlegl","year":"2019","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30\u201344 (2019). https:\/\/doi.org\/10.1016\/j.media.2019.01.010","journal-title":"Med. Image Anal."},{"key":"3056_CR43","doi-asserted-by":"crossref","unstructured":"He K., Zhang X., Ren S., and Sun J.: Deep Residual Learning for Image Recognition. In : Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp, 770\u2013778. IEEE (2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"3056_CR44","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.: Fully convolutional networks for semantic segmentation. IEEE Trans. Patt. Anal. Mach. Intell. 39, 640\u2013651 (2017)","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."},{"key":"3056_CR45","unstructured":"Simonyan K. and Zisserman A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"issue":"12","key":"3056_CR46","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Patt. Anal. Mach. Intell. 39(12), 2481\u20132495 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."},{"key":"3056_CR47","unstructured":"Elhassan M. A. M., Yang C., Huang C., and Legesse Munea T.: SPFNet:Subspace Pyramid Fusion Network for Semantic Segmentation. arXiv e-prints, arXiv:2204.01278 (2022)."},{"key":"3056_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tim.2020.3040485","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). https:\/\/doi.org\/10.1109\/tim.2020.3040485","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"3056_CR49","unstructured":"Yu F. and Koltun V.: Multi-Scale Context Aggregation by Dilated Convolutions. arXiv e-prints, arXiv:1511.07122."},{"key":"3056_CR50","doi-asserted-by":"crossref","unstructured":"Chen L.-C., Zhu Y., Papandreou G., Schroff F., and Adam H.: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In: Proceddings of the European Conference on Computer Vision (ECCV), pp. 833\u2013851. Springer International Publishing (2018) .","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"3056_CR51","doi-asserted-by":"publisher","unstructured":"Zhao H., Shi J., Qi X., Wang X., and Jia J.: Pyramid Scene Parsing Network. In: Procedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6230\u20136239. IEEE (2017). doi: https:\/\/doi.org\/10.1109\/CVPR.2017.660.","DOI":"10.1109\/CVPR.2017.660"},{"key":"3056_CR52","doi-asserted-by":"publisher","unstructured":"Lin G., Milan A., Shen C., and Reid I.: RefineNet: Multi-path refinement networks for high-resolution semantic segmentation. In: Procedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 5168\u20135177. IEEE (2017). doi: https:\/\/doi.org\/10.1109\/CVPR.2017.549.","DOI":"10.1109\/CVPR.2017.549"},{"key":"3056_CR53","doi-asserted-by":"crossref","unstructured":"Yu C., Wang J., Peng C., Gao C., Yu G., and Sang N.: BiSeNet: bilateral segmentation network for real-time semantic segmentation. In: Proceddings of the European conference on computer vision (ECCV), pp. 334\u2013349. Springer International Publishing (2017).","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"3056_CR54","doi-asserted-by":"publisher","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., & Lu, H.: Dual Attention Network for Scene Segmentation. In: proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp. 3141\u20133149. IEEE (2019). doi: https:\/\/doi.org\/10.1109\/CVPR.2019.00326.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"3056_CR55","doi-asserted-by":"crossref","unstructured":"Lu Y., Chen Y., Zhao D., and Chen J.: Graph-FCN for Image Semantic Segmentation. In: advances in neural networks \u2013 ISNN 2019, pp. 97\u2013105. Springer International Publishing (2019).","DOI":"10.1007\/978-3-030-22796-8_11"},{"key":"3056_CR56","doi-asserted-by":"crossref","unstructured":"Huang G., Liu Z., Van Der Maaten L., and Weinberger K. Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 4700\u20134708. IEEE (2017).","DOI":"10.1109\/CVPR.2017.243"},{"key":"3056_CR57","doi-asserted-by":"crossref","unstructured":"Zhang X., Zhou X., Lin M., and Sun J.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 6848\u20136856. IEEE (2018).","DOI":"10.1109\/CVPR.2018.00716"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-03056-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-03056-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-03056-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T09:12:10Z","timestamp":1713517930000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-03056-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,21]]},"references-count":57,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["3056"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-03056-w","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,21]]},"assertion":[{"value":"6 August 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2023","order":2,"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"}}]}}