{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T13:29:38Z","timestamp":1756992578048,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031189128"},{"type":"electronic","value":"9783031189135"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-18913-5_53","type":"book-chapter","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T23:03:53Z","timestamp":1666825433000},"page":"691-703","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dirt Detection and\u00a0Segmentation Network for\u00a0Autonomous Washing Robots"],"prefix":"10.1007","author":[{"given":"Shangbin","family":"Guan","sequence":"first","affiliation":[]},{"given":"Gang","family":"Peng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,27]]},"reference":[{"key":"53_CR1","unstructured":"Richard, B., et al.: Autonomous dirt detection for cleaning in office environments. In: International Conference on Robotics and Automation, pp. 1260\u20131267. IEEE (2013)"},{"key":"53_CR2","unstructured":"J\u00fcrgen, H., et al.: A probabilistic approach to high-confidence cleaning guarantees for low-cost cleaning robots. In: International Conference on Robotics and Automation (ICRA), pp. 5600\u20135605. IEEE (2014)"},{"key":"53_CR3","doi-asserted-by":"crossref","unstructured":"Redmon, J., et al.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"53_CR4","unstructured":"Shaoqing, R., et al.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"53_CR5","unstructured":"Richard, B., et al.: DirtNet: visual dirt detection for autonomous cleaning robots. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 1977\u20131983 (2020)"},{"issue":"4","key":"53_CR6","doi-asserted-by":"publisher","first-page":"94","DOI":"10.3390\/technologies9040094","volume":"9","author":"C Daniel","year":"2021","unstructured":"Daniel, C., et al.: A deep learning-based dirt detection computer vision system for floor-cleaning robots with improved data collection. Technologies 9(4), 94 (2021)","journal-title":"Technologies"},{"key":"53_CR7","unstructured":"Glenn, J., et al.: ultralytics\/yolov5: v5. 0-YOLOv5-P6 1280 models. AWS, Supervise. ly and YouTube integrations 10 (2021)"},{"key":"53_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/978-3-642-15555-0_6","volume-title":"Computer Vision \u2013 ECCV 2010","author":"J Deng","year":"2010","unstructured":"Deng, J., Berg, A.C., Li, K., Fei-Fei, L.: What does classifying more than 10,000 image categories tell Us? In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 71\u201384. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15555-0_6"},{"key":"53_CR9","doi-asserted-by":"crossref","unstructured":"Stefan, H., et al.: Dominant orientation templates for real-time detection of texture-less objects. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2257\u20132264. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5539908"},{"issue":"10","key":"53_CR10","doi-asserted-by":"publisher","first-page":"1284","DOI":"10.1177\/0278364911401765","volume":"30","author":"C Alvaro","year":"2011","unstructured":"Alvaro, C., et al.: The MOPED framework: object recognition and pose estimation for manipulation. Int. J. Rob. Res. 30(10), 1284\u20131306 (2011)","journal-title":"Int. J. Rob. Res."},{"key":"53_CR11","unstructured":"Jan, F., et al.: A framework for object training and 6 DoF pose estimation. In: 7th German Conference on Robotics, pp. 1\u20136. VDE (2012)"},{"issue":"5","key":"53_CR12","first-page":"817","volume":"7","author":"R Vert","year":"2006","unstructured":"Vert, R., et al.: Consistency and convergence rates of one-class SVMs and related algorithms. J. Mach. Learn. Res. 7(5), 817\u2013854 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"53_CR13","unstructured":"Platt, J.C. Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines (1998)"},{"issue":"1","key":"53_CR14","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","volume":"54","author":"DMJ Tax","year":"2004","unstructured":"Tax, D.M.J., Duin, R.P.W.: Support vector data description. Mach. Learn. 54(1), 45\u201366 (2004)","journal-title":"Mach. Learn."},{"key":"53_CR15","volume-title":"Machine learning","author":"Z Zhou","year":"2016","unstructured":"Zhou, Z., et al.: Machine learning, 1st edn. Tsinghua University Press, Beijing (2016)","edition":"1"},{"issue":"3","key":"53_CR16","doi-asserted-by":"publisher","first-page":"1171","DOI":"10.1214\/009053607000000677","volume":"36","author":"T Hofmann","year":"2008","unstructured":"Hofmann, T., et al.: Kernel methods in machine learning. Ann. Stat. 36(3), 1171\u20131220 (2008)","journal-title":"Ann. Stat."},{"key":"53_CR17","unstructured":"Ruff, L., et al.: Deep one-class classification. In: International Conference on Machine Learning pp. 4393\u20134402. PMLR (2018)"},{"key":"53_CR18","unstructured":"Ruff, L., et al.: Deep semi-supervised anomaly detection. arXiv preprint arXiv:1906.02694 (2019)"},{"key":"53_CR19","unstructured":"Jihun, Y., et al.: Patch svdd: patch-level svdd for anomaly detection and segmentation. In: Proceedings of the Asian Conference on Computer Vision (2020)"},{"key":"53_CR20","unstructured":"Paul, B., et al.: MVTec AD-a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9592\u20139600 (2019)"},{"issue":"4","key":"53_CR21","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1007\/s11263-020-01400-4","volume":"129","author":"P Bergmann","year":"2021","unstructured":"Bergmann, P., Batzner, K., Fauser, M., Sattlegger, D., Steger, C.: The MVTec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection. Int. J. Comput. Vision 129(4), 1038\u20131059 (2021). https:\/\/doi.org\/10.1007\/s11263-020-01400-4","journal-title":"Int. J. Comput. Vision"},{"key":"53_CR22","unstructured":"Philipp, L., et al.: Explainable deep one-class classification. arXiv preprint arXiv:2007.01760 (2020)"},{"key":"53_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1007\/978-3-319-64107-2_34","volume-title":"Towards Autonomous Robotic Systems","author":"A Gr\u00fcnauer","year":"2017","unstructured":"Gr\u00fcnauer, A., Halmetschlager-Funek, G., Prankl, J., Vincze, M.: The power of GMMs: unsupervised dirt spot detection for industrial floor cleaning robots. In: Gao, Y., Fallah, S., Jin, Y., Lekakou, C. (eds.) TAROS 2017. LNCS (LNAI), vol. 10454, pp. 436\u2013449. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-64107-2_34"},{"key":"53_CR24","doi-asserted-by":"crossref","unstructured":"Hansi, J., et al.: Fast incremental SVDD learning algorithm with the Gaussian kernel. In: AAAI Conference on Artificial Intelligence, vol. 33(01), pp. 3991\u20133998 (2019)","DOI":"10.1609\/aaai.v33i01.33013991"},{"key":"53_CR25","unstructured":"Carl, D., et al.: Unsupervised visual representation learning by context prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1422\u20131430 (2015)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18913-5_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T16:35:39Z","timestamp":1728232539000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18913-5_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031189128","9783031189135"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18913-5_53","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"27 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/en.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"564","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"233","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.03","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.35","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}