{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T22:28:47Z","timestamp":1783808927021,"version":"3.55.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T00:00:00Z","timestamp":1681776000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T00:00:00Z","timestamp":1681776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Natural Science Research for Youth Program of Shanxi Province","award":["202103021223308"],"award-info":[{"award-number":["202103021223308"]}]},{"name":"the Key Laboratory of Biomedical Imaging and Big Data of Shanxi Province Open Research Project under Grant KF","award":["2020-12"],"award-info":[{"award-number":["2020-12"]}]},{"name":"the Natural Science Research Program of Shanxi Province","award":["202103021224265"],"award-info":[{"award-number":["202103021224265"]}]},{"name":"the Natural Science Research Program of Shanxi Province","award":["202203021211333"],"award-info":[{"award-number":["202203021211333"]}]},{"name":"the Natural Science for Youth Foundation of China","award":["62001321"],"award-info":[{"award-number":["62001321"]}]},{"name":"the Research Project Supported by Shanxi Scholarship Council of China","award":["2021-111"],"award-info":[{"award-number":["2021-111"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s11760-022-02475-x","type":"journal-article","created":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T06:04:04Z","timestamp":1681797844000},"page":"2583-2593","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Surface defect detection of solar cell based on similarity non-maximum suppression mechanism"],"prefix":"10.1007","volume":"17","author":[{"given":"Yanling","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Shangguan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengcheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoyue","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,4,18]]},"reference":[{"key":"2475_CR1","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1016\/j.optlastec.2010.01.022","volume":"42","author":"Q Li","year":"2010","unstructured":"Li, Q., Wang, W., Ma, C., Zhu, Z.: Detection of physical defects in solar cells by hyper spectral imaging technology. Opt. Laser Technol. 42, 1010\u20131013 (2010)","journal-title":"Opt. Laser Technol."},{"key":"2475_CR2","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.solener.2015.12.029","volume":"126","author":"K Drabczyk","year":"2016","unstructured":"Drabczyk, K., Kulesza-Matlak, G., Dryga\u0142a, A., et al.: Electroluminescence imaging for determining the influence of metallization parameters for solar cell metal contacts. Sol. Energy 126, 14\u201321 (2016)","journal-title":"Sol. Energy"},{"issue":"4","key":"2475_CR3","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1109\/T-ED.1980.19948","volume":"27","author":"DE Sawyer","year":"1980","unstructured":"Sawyer, D.E., Kessler, H.K.: Laser scanning of solar cells for the display of cell operating characteristics and detection of cell defects. IEEE Trans. Electron Devices 27(4), 864\u2013872 (1980)","journal-title":"IEEE Trans. Electron Devices"},{"issue":"8","key":"2475_CR4","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1016\/j.apacoust.2007.03.002","volume":"69","author":"C Hilmersson","year":"2008","unstructured":"Hilmersson, C., Hess, D.P., Dallas, W., Ostapenko, S.: Crack detection in single-crystalline silicon wafers using impact testing. Appl. Acoust. 69(8), 755\u2013760 (2008)","journal-title":"Appl. Acoust."},{"issue":"5","key":"2475_CR5","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1109\/TTHZ.2014.2330977","volume":"4","author":"CY Jen","year":"2014","unstructured":"Jen, C.Y., Richter, C.: Doping profile recognition applied to silicon photovoltaic cells using terahertz time-domain spectroscopy. IEEE Trans. Terahertz Sci. Technol. 4(5), 560\u2013567 (2014)","journal-title":"IEEE Trans. Terahertz Sci. Technol."},{"issue":"3","key":"2475_CR6","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1016\/j.aei.2015.01.014","volume":"29","author":"DM Tsai","year":"2015","unstructured":"Tsai, D.M., Li, G.N., Li, W.C., et al.: Defect detection in multi-crystal solar cells using clustering with uniformity measures. Adv. Eng. Inf. 29(3), 419\u2013430 (2015)","journal-title":"Adv. Eng. Inf."},{"issue":"3","key":"2475_CR7","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1108\/SR-08-2017-0166","volume":"38","author":"X Qian","year":"2018","unstructured":"Qian, X., Zhang, H., Yang, C., et al.: Micro-cracks detection of multicrystalline solar cell surface based on self-learning features and low-rank matrix recovery. Sens. Rev. 38(3), 360\u2013368 (2018)","journal-title":"Sens. Rev."},{"issue":"2","key":"2475_CR8","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1016\/j.patcog.2011.07.025","volume":"45","author":"WC Li","year":"2012","unstructured":"Li, W.C., Tsai, D.M.: Wavelet-based defect detection in solar wafer images with inhomogeneous texture. Pattern Recogn. 45(2), 742\u2013756 (2012)","journal-title":"Pattern Recogn."},{"issue":"7553","key":"2475_CR9","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"issue":"2","key":"2475_CR10","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","volume":"128","author":"L Liu","year":"2020","unstructured":"Liu, L., Ouyang, W., Wang, X., et al.: Deep learning for generic object detection: a survey. Int. J. Comput. Vis. 128(2), 261\u2013318 (2020)","journal-title":"Int. J. Comput. Vis."},{"key":"2475_CR11","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"2475_CR12","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"2475_CR13","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263\u20137271 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"2475_CR14","unstructured":"Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"key":"2475_CR15","unstructured":"Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)"},{"key":"2475_CR16","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., et al.: TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2778\u20132788 (2021)","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"2475_CR17","first-page":"21","volume-title":"SSD: single shot multibox detector. European conference on computer vision","author":"W Liu","year":"2016","unstructured":"Liu, W., Anguelov, D., Erhan, D., et al.: SSD: single shot multibox detector. European conference on computer vision, pp. 21\u201337. Springer, Cham (2016)"},{"issue":"9","key":"2475_CR18","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904\u20131916 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2475_CR19","doi-asserted-by":"crossref","unstructured":"Girshick, R., Iandola, F., Darrell, T., et al.: Deformable part models are convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 437\u2013446 (2015)","DOI":"10.1109\/CVPR.2015.7298641"},{"key":"2475_CR20","unstructured":"Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28, 91\u201399 (2015)"},{"key":"2475_CR21","unstructured":"Dai, J., Li, Y., He, K., et al.: R-FCN: object detection via region-based fully convolutional networks. Adv. Neural Inf. Process. Syst. 29 (2016)"},{"key":"2475_CR22","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154\u20136162 (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"2475_CR23","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., et al.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"issue":"12","key":"2475_CR24","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., et al.: PGANet: Pyramid feature fusion and global context attention network for automated surface defect detection. IEEE Trans. Industr. Inform. 16(12), 7448\u20137458 (2020)","journal-title":"IEEE Trans. Industr. Inform."},{"key":"2475_CR25","doi-asserted-by":"crossref","unstructured":"\u00dczen, H., Turkoglu, M., Aslan, M., et al.: Depth-wise squeeze and excitation block-based efficient-Unet model for surface defect detection. Vis Comput 1\u201320 (2022)","DOI":"10.1007\/s00371-022-02442-0"},{"key":"2475_CR26","doi-asserted-by":"publisher","first-page":"15884","DOI":"10.1109\/ACCESS.2019.2894420","volume":"716","author":"L Qiu","year":"2019","unstructured":"Qiu, L., Wu, X., Yu, Z.: A high-efficiency fully convolutional networks for pixel-wise surface defect detection. IEEE Access 7, 15884\u201315893 (2019)","journal-title":"IEEE Access"},{"key":"2475_CR27","doi-asserted-by":"publisher","first-page":"118269","DOI":"10.1016\/j.eswa.2022.118269","volume":"209","author":"H \u00dczen","year":"2022","unstructured":"\u00dczen, H., T\u00fcrko\u011flu, M., Yanikoglu, B., et al.: Swin-MFINet: Swin transformer based multi-feature integration network for detection of pixel-level surface defects. Expert Syst. Appl. 209, 118269 (2022)","journal-title":"Expert Syst. Appl."},{"key":"2475_CR28","doi-asserted-by":"crossref","unstructured":"Bartler, A., Mauch, L., Yang, B., et al.: Automated detection of solar cell defects with deep learning. In: 2018 26th European Signal Processing Conference (EUSIPCO), IEEE, pp. 2035\u20132039 (2018)","DOI":"10.23919\/EUSIPCO.2018.8553025"},{"issue":"2","key":"2475_CR29","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/s10845-018-1458-z","volume":"31","author":"H Chen","year":"2020","unstructured":"Chen, H., Pang, Y., Hu, Q., et al.: Solar cell surface defect inspection based on multispectral convolutional neural network. J. Intell. Manuf. 31(2), 453\u2013468 (2020)","journal-title":"J. Intell. Manuf."},{"key":"2475_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2020.103334","volume":"108","author":"X Zhang","year":"2020","unstructured":"Zhang, X., Hao, Y., Shangguan, H., et al.: Detection of surface defects on solar cells by fusing Multi-channel convolution neural networks. Infrared Phys. Technol. 108, 103334 (2020)","journal-title":"Infrared Phys. Technol."},{"key":"2475_CR31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2475_CR32","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, Y., Wang, N., et al.: Scale-aware trident networks for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6054\u20136063 (2019)","DOI":"10.1109\/ICCV.2019.00615"},{"key":"2475_CR33","unstructured":"He, Y., Zhang, X., Savvides, M., et al.: Softer-NMS: rethinking bounding box regression for accurate object detection. arXiv preprint arXiv:1809.08545, vol. 2, no. 3, pp. 69\u201380 (2018)"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02475-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-022-02475-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02475-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:33:15Z","timestamp":1684369995000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-022-02475-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,18]]},"references-count":33,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["2475"],"URL":"https:\/\/doi.org\/10.1007\/s11760-022-02475-x","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-2238544\/v1","asserted-by":"object"}]},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,18]]},"assertion":[{"value":"4 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 December 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2023","order":4,"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 no conflicts of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}