{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T11:50:34Z","timestamp":1777895434861,"version":"3.51.4"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:00:00Z","timestamp":1735862400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:00:00Z","timestamp":1735862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Open Fund of Shandong Key Laboratory of Mining Machinery Engineering","award":["2022KLMM303"],"award-info":[{"award-number":["2022KLMM303"]}]},{"name":"the Open Fund of Shandong Key Laboratory of Mining Machinery Engineering","award":["2022KLMM303"],"award-info":[{"award-number":["2022KLMM303"]}]},{"name":"the Open Fund of Shandong Key Laboratory of Mining Machinery Engineering","award":["2022KLMM303"],"award-info":[{"award-number":["2022KLMM303"]}]},{"name":"the Open Fund of Shandong Key Laboratory of Mining Machinery Engineering","award":["2022KLMM303"],"award-info":[{"award-number":["2022KLMM303"]}]},{"name":"the Open Fund of Shandong Key Laboratory of Mining Machinery Engineering","award":["2022KLMM303"],"award-info":[{"award-number":["2022KLMM303"]}]},{"name":"the Shandong Key Research and Development Program","award":["2023CXPT062"],"award-info":[{"award-number":["2023CXPT062"]}]},{"name":"the Shandong Key Research and Development Program","award":["2023CXPT062"],"award-info":[{"award-number":["2023CXPT062"]}]},{"name":"the Shandong Key Research and Development Program","award":["2023CXPT062"],"award-info":[{"award-number":["2023CXPT062"]}]},{"name":"the Shandong Key Research and Development Program","award":["2023CXPT062"],"award-info":[{"award-number":["2023CXPT062"]}]},{"name":"the Shandong Key Research and Development Program","award":["2023CXPT062"],"award-info":[{"award-number":["2023CXPT062"]}]},{"name":"the Major Special Project for Science and Technology Innovation in Tai'an City","award":["2022ZDZX010"],"award-info":[{"award-number":["2022ZDZX010"]}]},{"name":"the Major Special Project for Science and Technology Innovation in Tai'an City","award":["2022ZDZX010"],"award-info":[{"award-number":["2022ZDZX010"]}]},{"name":"the Major Special Project for Science and Technology Innovation in Tai'an City","award":["2022ZDZX010"],"award-info":[{"award-number":["2022ZDZX010"]}]},{"name":"the Major Special Project for Science and Technology Innovation in Tai'an City","award":["2022ZDZX010"],"award-info":[{"award-number":["2022ZDZX010"]}]},{"name":"the Major Special Project for Science and Technology Innovation in Tai'an City","award":["2022ZDZX010"],"award-info":[{"award-number":["2022ZDZX010"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s11554-024-01614-6","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T01:36:00Z","timestamp":1735868160000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["D2net: a dual-branch lightweight network for conveyor belt rotation detection in pipe belt conveyors"],"prefix":"10.1007","volume":"22","author":[{"given":"Xingyu","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nini","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Yun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengchao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeqing","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,3]]},"reference":[{"key":"1614_CR1","doi-asserted-by":"crossref","unstructured":"Hao, N., Wang, X., Zhang, Y., Zhang, M., Sun, C., Hu, C.: A machine vision-based edge detection method for belt lap of pipe belt conveyor. In: Intelligent Equipment and Special Robots (IOS Press, 2024), pp. 80\u201386","DOI":"10.3233\/ATDE240225"},{"key":"1614_CR2","unstructured":"Will, F., Staribacher, J.: Pipe conveyors transport bulk material efficiently over long distances; Rohrgurtfoerderer transportieren Schuettgut effizient ueber lange Strecken, World of Mining-Surface and Underground 63 (2011)"},{"issue":"3","key":"1614_CR3","first-page":"174","volume":"23","author":"R Horak","year":"2003","unstructured":"Horak, R.: A new technology for pipe or tube conveyors. Bulk Solids Handl. 23(3), 174 (2003)","journal-title":"Bulk Solids Handl."},{"key":"1614_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.engfailanal.2020.104703","volume":"116","author":"V Molnar","year":"2020","unstructured":"Molnar, V., Fedorko, G., Honus, S., Andrejiova, M., Grincova, A., Michalik, P., Palencar, J.: Research in placement of measuring sensors on hexagonal idler housing with regard to requirements of pipe conveyor failure analysis. Eng. Fail. Anal. 116, 104703 (2020)","journal-title":"Eng. Fail. Anal."},{"key":"1614_CR5","doi-asserted-by":"publisher","first-page":"238","DOI":"10.4028\/www.scientific.net\/AMM.683.238","volume":"683","author":"P Michalik","year":"2014","unstructured":"Michalik, P., Zajac, J.: Use of thermovision for monitoring temperature conveyor belt of pipe conveyor. Appl. Mech. Mater. 683, 238 (2014)","journal-title":"Appl. Mech. Mater."},{"issue":"24","key":"1614_CR6","doi-asserted-by":"publisher","first-page":"9504","DOI":"10.3390\/en15249504","volume":"15","author":"Q Mao","year":"2022","unstructured":"Mao, Q., Li, S., Hu, X., Xue, X.: Coal mine belt conveyor foreign objects recognition method of improved YOLOv5 algorithm with defogging and deblurring. Energies 15(24), 9504 (2022)","journal-title":"Energies"},{"key":"1614_CR7","doi-asserted-by":"crossref","unstructured":"Miao, D., Wang, Y., Yang, L., Wei, S.: Coal flow detection of belt conveyor based on the two-dimensional laser. IEEE Access (2023)","DOI":"10.1109\/ACCESS.2023.3301768"},{"key":"1614_CR8","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.aej.2023.03.034","volume":"71","author":"X Guo","year":"2023","unstructured":"Guo, X., Liu, X., Gardoni, P., Glowacz, A., Kr\u00f3lczyk, G., Incecik, A., Li, Z.: Machine vision based damage detection for conveyor belt safety using Fusion knowledge distillation. Alexand. Eng. J. 71, 161 (2023)","journal-title":"Alexand. Eng. J."},{"key":"1614_CR9","doi-asserted-by":"crossref","unstructured":"Wang, J., Liu, Q., Dai, M.: Belt vision localization algorithm based on machine vision and belt conveyor deviation detection. In: 2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) (IEEE, 2019), pp. 269\u2013273","DOI":"10.1109\/YAC.2019.8787667"},{"key":"1614_CR10","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"1614_CR11","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"1614_CR12","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 (Springer, 2015), pp. 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"12","key":"1614_CR13","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. Pattern Anal. Mach. Intell. 39(12), 2481 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1614_CR14","unstructured":"Chen, L.C.: Rethinking atrous convolution for semantic image segmentation (2017). arXiv preprint arXiv:1706.05587"},{"key":"1614_CR15","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation In: Proceedings of the European conference on computer vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"1614_CR16","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation (2021). arXiv preprint arXiv:2102.04306"},{"key":"1614_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 10,012\u201310,022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1614_CR18","unstructured":"Dong, B., Wang, W., Fan, D.P., Li, J., Fu, H., Shao, L.: Polyp-pvt: Polyp segmentation with pyramid vision transformers (2021). arXiv preprint arXiv:2108.06932"},{"key":"1614_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2020.102918","volume":"72","author":"L Xiao","year":"2020","unstructured":"Xiao, L., Wu, B., Hu, Y.: OSED: object-specific edge detection. J. Vis. Commun. Image Represent. 72, 102918 (2020)","journal-title":"J. Vis. Commun. Image Represent."},{"issue":"13","key":"1614_CR20","doi-asserted-by":"publisher","first-page":"7181","DOI":"10.1109\/JSEN.2020.2977366","volume":"20","author":"L Xiao","year":"2020","unstructured":"Xiao, L., Wu, B., Hu, Y.: Surface defect detection using image pyramid. IEEE Sens. J. 20(13), 7181 (2020)","journal-title":"IEEE Sens. J."},{"key":"1614_CR21","first-page":"1","volume":"70","author":"L Xiao","year":"2020","unstructured":"Xiao, L., Wu, B., Hu, Y.: Missing small fastener detection using deep learning. IEEE Trans. Instrum. Measure. 70, 1 (2020)","journal-title":"IEEE Trans. Instrum. Measure."},{"key":"1614_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109145","volume":"137","author":"M Gao","year":"2024","unstructured":"Gao, M., Li, S., Chen, X., Wang, Y.: A novel combined method for conveyor belt deviation discrimination under complex operational scenarios. Eng. Appl. Artif. Intell. 137, 109145 (2024)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"11","key":"1614_CR23","doi-asserted-by":"publisher","first-page":"10870","DOI":"10.1109\/JSEN.2022.3170971","volume":"22","author":"M Liu","year":"2022","unstructured":"Liu, M., Zhu, Q., Yin, Y., Fan, Y., Su, Z., Zhang, S.: Damage detection method of mining conveyor belt based on deep learning. IEEE Sens. J. 22(11), 10870 (2022)","journal-title":"IEEE Sens. J."},{"issue":"1","key":"1614_CR24","doi-asserted-by":"publisher","first-page":"3734560","DOI":"10.1155\/2021\/3734560","volume":"2021","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Miao, C., Li, X., Xu, G.: Research on deviation detection of belt conveyor based on inspection robot and deep learning. Complexity 2021(1), 3734560 (2021)","journal-title":"Complexity"},{"issue":"19","key":"1614_CR25","doi-asserted-by":"publisher","first-page":"8002","DOI":"10.1016\/j.ijleo.2016.05.111","volume":"127","author":"J Li","year":"2016","unstructured":"Li, J., Miao, C.: The conveyor belt longitudinal tear on-line detection based on improved SSR algorithm. Optik 127(19), 8002 (2016)","journal-title":"Optik"},{"key":"1614_CR26","doi-asserted-by":"crossref","unstructured":"Xu, C., Zeng, X., Zhang, R., Wang, K.: Detection method of edge position of belt conveyor based on complex environment. In: 2021 4th International Conference on Robotics, Control and Automation Engineering (RCAE) (IEEE, 2021), pp. 417\u2013422","DOI":"10.1109\/RCAE53607.2021.9638894"},{"issue":"4","key":"1614_CR27","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1080\/19392699.2022.2072306","volume":"43","author":"B Wang","year":"2023","unstructured":"Wang, B., Dou, D., Shen, N.: An intelligent belt wear fault diagnosis method based on deep learning. Int. J. Coal Prepar. Utiliz. 43(4), 708 (2023)","journal-title":"Int. J. Coal Prepar. Utiliz."},{"key":"1614_CR28","unstructured":"Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: Enet: A deep neural network architecture for real-time semantic segmentation (2016). arXiv preprint arXiv:1606.02147"},{"key":"1614_CR29","doi-asserted-by":"publisher","first-page":"5175","DOI":"10.1109\/TIP.2020.2976856","volume":"29","author":"Z Yang","year":"2020","unstructured":"Yang, Z., Yu, H., Feng, M., Sun, W., Lin, X., Sun, M., Mao, Z.H., Mian, A.: Small object augmentation of urban scenes for real-time semantic segmentation. IEEE Trans. Image Process. 29, 5175 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"1614_CR30","doi-asserted-by":"crossref","unstructured":"Zhao, H., Qi, X., Shen, X., Shi, J., Jia, J.: Icnet for real-time semantic segmentation on high-resolution images. In: Proceedings of the European conference on computer vision (ECCV), pp. 405\u2013420 (2018)","DOI":"10.1007\/978-3-030-01219-9_25"},{"key":"1614_CR31","doi-asserted-by":"crossref","unstructured":"Mehta, S., Rastegari, M., Caspi, A., Shapiro, L., Hajishirzi, H.: Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation. In: Proceedings of the european conference on computer vision (ECCV), pp. 552\u2013568 (2018)","DOI":"10.1007\/978-3-030-01249-6_34"},{"key":"1614_CR32","doi-asserted-by":"publisher","first-page":"5079","DOI":"10.1109\/TIP.2020.2978583","volume":"29","author":"B Jiang","year":"2020","unstructured":"Jiang, B., Tu, W., Yang, C., Yuan, J.: Context-integrated and feature-refined network for lightweight object parsing. IEEE Trans. Image Process. 29, 5079 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"1614_CR33","doi-asserted-by":"crossref","unstructured":"Li, H., Xiong, P., Fan, H., Sun, J.: Dfanet: deep feature aggregation for real-time semantic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 9522\u20139531 (2019)","DOI":"10.1109\/CVPR.2019.00975"},{"key":"1614_CR34","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation In: Proceedings of the European conference on computer vision (ECCV), pp. 325\u2013341 (2018)","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"1614_CR35","unstructured":"Poudel, R.P., Liwicki, S., Cipolla, R.: Fast-scnn: Fast semantic segmentation network (2019). arXiv preprint arXiv:1902.04502"},{"issue":"2","key":"1614_CR36","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s11554-023-01295-7","volume":"20","author":"Z Lv","year":"2023","unstructured":"Lv, Z., Li, Y., Qian, S.: Real-time and accurate defect segmentation of aluminum strip surface via a lightweight network. J. Real Time Image Process. 20(2), 37 (2023)","journal-title":"J. Real Time Image Process."},{"issue":"4","key":"1614_CR37","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/s11554-024-01502-z","volume":"21","author":"X Zhao","year":"2024","unstructured":"Zhao, X., Zeng, M., Dong, Y., Rao, G., Huang, X., Mo, X.: FastBeltNet: a dual-branch light-weight network for real-time conveyor belt edge detection. J. Real Time Image Process. 21(4), 123 (2024)","journal-title":"J. Real Time Image Process."},{"key":"1614_CR38","doi-asserted-by":"crossref","unstructured":"Orsic, M., Kreso, I., Bevandic, P., Segvic, S.: In defense of pre-trained imagenet architectures for real-time semantic segmentation of road-driving images. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 12,607\u201312,616 (2019)","DOI":"10.1109\/CVPR.2019.01289"},{"key":"1614_CR39","unstructured":"Wu, H., Zhang, J., Huang, K., Liang, K., Yu, Y.: Fastfcn: Rethinking dilated convolution in the backbone for semantic segmentation (2019). arXiv preprint arXiv:1903.11816"},{"key":"1614_CR40","doi-asserted-by":"crossref","unstructured":"Fan, M., Lai, S., Huang, J., Wei, X., Chai, Z., Luo, J., Wei, X.: Rethinking bisenet for real-time semantic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 9716\u20139725 (2021)","DOI":"10.1109\/CVPR46437.2021.00959"},{"key":"1614_CR41","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. Adv. Neural Inform. Process. Syst. 34, 12077 (2021)","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"1614_CR42","doi-asserted-by":"crossref","unstructured":"Xu, J., Xiong, Z., Bhattacharyya, S.P.: PIDNet: A real-time semantic segmentation network inspired by PID controllers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 19,529\u201319,539 (2023)","DOI":"10.1109\/CVPR52729.2023.01871"},{"key":"1614_CR43","doi-asserted-by":"crossref","unstructured":"Xu, Z., Wu, D., Yu, C., Chu, X., Sang, N., Gao, C.: SCTNet: Single-Branch CNN with Transformer Semantic Information for Real-Time Segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 6378\u20136386 (2024)","DOI":"10.1609\/aaai.v38i6.28457"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01614-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01614-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01614-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T12:16:48Z","timestamp":1738585008000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01614-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,3]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["1614"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01614-6","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-5417187\/v1","asserted-by":"object"}]},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,3]]},"assertion":[{"value":"8 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2025","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"}}],"article-number":"37"}}