<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>35557</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>18</pageLast>
    <pageNumber>18</pageNumber>
    <edition/>
    <issue/>
    <volume>2503.10356</volume>
    <type>articlenr</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-03-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Object detection characteristics in a learning factory environment using YOLOv8</title>
    <abstract language="eng">AI-based object detection, and efforts to explain and investigate their characteristics, is a topic of high interest. The impact of, e.g., complex background structures with similar appearances as the objects of interest, on the detection accuracy and, beforehand, the necessary dataset composition are topics of ongoing research. In this paper, we present a systematic investigation of background influences and different features of the object to be detected. The latter includes various materials and surfaces, partially transparent and with shiny reflections in the context of an Industry 4.0 learning factory. Different YOLOv8 models have been trained for each of the materials on different sized datasets, where the appearance was the only changing parameter. In the end, similar characteristics tend to show different behaviours and sometimes unexpected results. While some background components tend to be detected, others with the same features are not part of the detection. Additionally, some more precise conclusions can be drawn from the results. Therefore, we contribute a challenging dataset with detailed investigations on 92 trained YOLO models, addressing some issues on the detection accuracy and possible overfitting.</abstract>
    <identifier type="doi">https://doi.org/10.48550/arXiv.2503.10356</identifier>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Referiert">Beitrag ist nicht referiert / Article non peer-reviewed</enrichment>
    <enrichment key="Publikationsweg">Open Access</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>
      <firstName>Toni</firstName>
      <lastName>Schneidereit</lastName>
    </author>
    <submitter>
      <firstName>Alexander</firstName>
      <lastName>Köhler</lastName>
    </submitter>
    <author>
      <firstName>Stefan</firstName>
      <lastName>Gohrenz</lastName>
    </author>
    <author>
      <firstName>Michael</firstName>
      <lastName>Breuß</lastName>
    </author>
    <collection role="institutes" number="1303">FG Angewandte Mathematik</collection>
  </doc>
  <doc>
    <id>36660</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>288</pageFirst>
    <pageLast>308</pageLast>
    <pageNumber>21</pageNumber>
    <edition/>
    <issue/>
    <volume>2</volume>
    <type>conferenceobject_ref</type>
    <publisherName>Springer Nature Switzerland</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-11-07</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Object detection characteristics in a learning factory environment using YOLOv8</title>
    <abstract language="eng">AI-based object detection, and efforts to explain and investigate their characteristics, is a topic of high interest. The impact of, e.g., complex background structures with similar appearances as the objects of interest, on the detection accuracy and, beforehand, the necessary dataset composition are topics of ongoing research. In this study, we present a systematic investigation of background influences and different features of the object to be detected. The latter includes various materials and surfaces, partially transparent and with shiny reflections in the context of an Industry 4.0 learning factory. Different YOLOv8 models have been trained for each of the materials on different sized datasets, where the appearance was the only changing parameter. In the end, similar characteristics tend to show different behaviours and sometimes unexpected results. While some background components tend to be detected, others with the same features are not part of the detection. Additionally, some more precise conclusions can be drawn from the results. Therefore, we contribute a challenging dataset with detailed investigations on 92 trained YOLO models, addressing some issues on the detection accuracy and possible overfitting.</abstract>
    <parentTitle language="eng">Intelligent systems and applications : proceedings of the 2025 Intelligent Systems Conference (IntelliSys) : volume 2</parentTitle>
    <identifier type="doi">https://doi.org/10.1007/978-3-032-00071-2_18</identifier>
    <identifier type="isbn">978-3-032-00071-2</identifier>
    <identifier type="issn">2367-3370</identifier>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_import_data">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T21:55:36Z","timestamp":1757627736757,"version":"3.44.0"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032000705"},{"type":"electronic","value":"9783032000712"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-032-00071-2_18","type":"book-chapter","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T12:04:58Z","timestamp":1755777898000},"page":"288-308","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Object Detection Characteristics in\u00a0a\u00a0Learning Factory Environment using YOLOv8"],"prefix":"10.1007","author":[{"given":"Toni","family":"Schneidereit","sequence":"first","affiliation":[]},{"given":"Stefan","family":"Gohrenz","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Breu\u00df","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,22]]},"reference":[{"key":"18_CR1","doi-asserted-by":"publisher","unstructured":"Zou, Z., Chen, K., Shi, Z., Guo Y., Ye J.: Object detection in 20 Years: a survey. Proc. IEEE 111(3), 257\u2013276 (2023). https:\/\/doi.org\/10.1109\/JPROC.2023.3238524","DOI":"10.1109\/JPROC.2023.3238524"},{"key":"18_CR2","doi-asserted-by":"publisher","unstructured":"\u00c7\u0131nar, Z.M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., Safaei, B.: Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability 12(19), 8211 (2020). https:\/\/doi.org\/10.3390\/su12198211","DOI":"10.3390\/su12198211"},{"key":"18_CR3","doi-asserted-by":"publisher","unstructured":"Carvalho, T.P., Soares, F.A., Vita, R., Francisco, R.D.P., Basto, J.P., Alcal\u00e1, S.G.: A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 137, 106024 (2019). https:\/\/doi.org\/10.1016\/j.cie.2019.106024","DOI":"10.1016\/j.cie.2019.106024"},{"key":"18_CR4","doi-asserted-by":"publisher","unstructured":"Susto, G. A., Schirru, A., Pampuri, S., McLoone, S., Beghi, A.: Machine learning for predictive maintenance: a multiple classifier approach. IEEE Trans. Ind. Inform. 11(3), 812\u2013820 (2014). https:\/\/doi.org\/10.1109\/TII.2014.2349359","DOI":"10.1109\/TII.2014.2349359"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Dash, R., McMurtrey, M., Rebman, C., Kar, U. K.: Application of artificial intelligence in automation of supply chain management. J. Strategic Innov. Sustain. 14(3), 43\u201353 (2019). https:\/\/articlearchives.co\/index.php\/JSIS\/article\/view\/4867","DOI":"10.33423\/jsis.v14i3.2105"},{"key":"18_CR6","doi-asserted-by":"publisher","unstructured":"Sarc, R., Curtis, A., Kandlbauer, L., Khodier, K., Lorber, K.E., Pomberger, R.: Digitalisation and intelligent robotics in value chain of circular economy oriented waste management\u2013a review. Waste Manage. 95, 476\u2013492 (2019). https:\/\/doi.org\/10.1016\/j.wasman.2019.06.035","DOI":"10.1016\/j.wasman.2019.06.035"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Abele, E., et al.: Learning factories for future oriented research and education in manufacturing. CIRP Ann.-Manuf. Technol. 66, 803\u2013826 (2017)","DOI":"10.1016\/j.cirp.2017.05.005"},{"key":"18_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-46428-7","volume-title":"Learning Factories: Featuring New Concepts, Guidelines, Worldwide Best-Practice Examples","author":"E Abele","year":"2024","unstructured":"Abele, E., Metternich, J., Tisch, M., Kre\u00df, A.: Learning Factories: Featuring New Concepts, Guidelines, Worldwide Best-Practice Examples. Springer International Publishing, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-46428-7"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv (2013). arXiv:1311.2524","DOI":"10.1109\/CVPR.2014.81"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. arXiv (2017). arXiv:1703.06870","DOI":"10.1109\/ICCV.2017.322"},{"key":"18_CR11","unstructured":"Terven, J., Cordova-Esparza, D.: A comprehensive review of YOLO: from YOLOv1 to YOLOv8 and beyond. arXiv (2023). arXiv:2304.00501"},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. arXiv (2015). arXiv:1506.02640","DOI":"10.1109\/CVPR.2016.91"},{"key":"18_CR13","doi-asserted-by":"publisher","unstructured":"Andrianandrianina Johanesa, T.V., Equeter, L., Mahmoudi, S.A.: Survey on AI applications for product quality control and predictive maintenance in industry 4.0. Electronics 13(5), 976 (2024). https:\/\/doi.org\/10.3390\/electronics13050976","DOI":"10.3390\/electronics13050976"},{"key":"18_CR14","doi-asserted-by":"publisher","first-page":"3465","DOI":"10.1007\/s00170-017-0882-0","volume":"94","author":"T Wang","year":"2018","unstructured":"Wang, T., Chen, Y., Qiao, M., Snoussi, H.: A fast and robust convolutional neural network-based defect detection model in product quality control. Int. J. Adv. Manuf. Technol. 94, 3465\u20133471 (2018)","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"18_CR15","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.ifacol.2018.09.412","volume":"51","author":"J Li","year":"2018","unstructured":"Li, J., Su, Z., Geng, J., Yin, Y.: Real-time detection of steel strip surface defects based on improved yolo detection network. IFAC-PapersOnLine 51, 76\u201381 (2018)","journal-title":"IFAC-PapersOnLine"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Chen, Y., Ding, Y., Zhao, F., Zhang, E., Wu, Z., Shao, L.: Surface defect detection methods for industrial products: a review. Appl. Sci. 11(16), 7657 (2021)","DOI":"10.3390\/app11167657"},{"key":"18_CR17","doi-asserted-by":"publisher","unstructured":"Khan Mohammadi, M., Schneidereit, T., Yarahmadi, A.M., Breu\u00df, M.: Investigating training datasets of real and synthetic images for outdoor swimmer localisation with YOLO. AI 5, 576\u2013593 (2024). https:\/\/doi.org\/10.3390\/ai5020030","DOI":"10.3390\/ai5020030"},{"key":"18_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107010","volume":"198","author":"M Cheng","year":"2022","unstructured":"Cheng, M., Yuan, H., Wang, Q., Cai, Z., Liu, Y., Zhang, Y.: Application of deep learning in sheep behaviors recognition and influence analysis of training data characteristics on the recognition effect. Comput. Electron. Agric. 198, 107010 (2022)","journal-title":"Comput. Electron. Agric."},{"key":"18_CR19","doi-asserted-by":"publisher","unstructured":"Bo\u017cko, A., Ambroziak, L.: Influence of insufficient dataset augmentation on IoU and detection threshold in CNN training for object detection on aerial images. Sensors 22(23), 9080 (2022). https:\/\/doi.org\/10.3390\/s22239080","DOI":"10.3390\/s22239080"},{"key":"18_CR20","doi-asserted-by":"publisher","unstructured":"Liu, C., Tao, Y., Liang, J., Li, K., Chen, Y.: Object detection based on YOLO network. In: 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), 799\u2013803. IEEE (2018). https:\/\/doi.org\/10.1109\/ITOEC.2018.8740604","DOI":"10.1109\/ITOEC.2018.8740604"},{"key":"18_CR21","doi-asserted-by":"publisher","unstructured":"Zell, S., Schneidereit, T., F\u00fcgenschuh, A., Breu\u00df, M.: Advanced search and rescue operations for drowning swimmers using autonomous unmanned aircraft systems: location optimization, flight trajectory planning and image-based localisation. Cottbus Math. Preprints (2024). https:\/\/doi.org\/10.26127\/BTUOpen-6866","DOI":"10.26127\/BTUOpen-6866"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Schneidereit, S., Yarahmadi, A.M., Schneidereit, T., Breu\u00df, M., Gebauer, M.: YOLO-based object detection in industry 4.0 fischertechnik model environment. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol. 823. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-47724-9_1","DOI":"10.1007\/978-3-031-47724-9_1"},{"key":"18_CR23","doi-asserted-by":"publisher","unstructured":"Quach, L. D., Quoc, K. N., Quynh, A. N., Ngoc, H. T.: Evaluating the effectiveness of YOLO models in different sized object detection and feature-based classification of small objects. J. Adv. Inf. Technol. 14(5), 907\u2013917 (2023). https:\/\/doi.org\/10.12720\/jait.14.5.907-917","DOI":"10.12720\/jait.14.5.907-917"},{"key":"18_CR24","unstructured":"Python ftrobopy library, GitHub (Accessed 14 Jan 2025). https:\/\/github.com\/ftrobopy\/ftrobopy"},{"key":"18_CR25","unstructured":"Schubert G.: SAP Academic User Group Meeting, (Accessed 14 Jan 2025). https:\/\/repositum.tuwien.at\/bitstream\/20.500.12708\/654\/2\/Schubert%20Guido%20-%202019%20-%20fischertechnik%20Simulationsmodelle%20fuer%20IoT-Anwendungen.pdf"},{"key":"18_CR26","doi-asserted-by":"publisher","unstructured":"Terven, J., C\u00f3rdova-Esparza, D. M., Romero-Gonz\u00e1lez, J. A.: A comprehensive review of YOLO architectures in computer vision: from YOLOv1 to YOLOv8 and YOLO-NAS. Mach. Learn. Knowl. Extr. 5(4), 1680\u20131716 (2023). https:\/\/doi.org\/10.3390\/make5040083","DOI":"10.3390\/make5040083"},{"key":"18_CR27","doi-asserted-by":"publisher","unstructured":"Sohan, M., Sai Ram, T., Rami Reddy, C.V.: A review on YOLOv8 and its advancements. Data Intelligence and Cognitive Informatics. ICDICI 2023. Algorithms for Intelligent Systems. Springer, Singapore (2024). https:\/\/doi.org\/10.1007\/978-981-99-7962-2_39","DOI":"10.1007\/978-981-99-7962-2_39"},{"key":"18_CR28","unstructured":"Reis, D., Kupec, J., Hong, J., Daoudi, A. . Real-time flying object detection with YOLOv8 (2023). arXiv preprint arXiv:2305.09972"},{"key":"18_CR29","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904\u20131916 (2015). https:\/\/doi.org\/10.1109\/TPAMI.2015.2389824","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"18_CR30","unstructured":"Ultralytics: YOLOv8 overview (Accessed 14 Jan 2025). https:\/\/docs.ultralytics.com\/de\/models\/yolov8\/#supported-tasks-and-modes"},{"key":"18_CR31","unstructured":"Ultralytics: YOLOv11. GitHub (Accessed 14 Jan 2025). https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"18_CR32","unstructured":"HumanSignal: labelImg. GitHub (Accessed 14 Jan 2025). https:\/\/github.com\/HumanSignal\/labelImg"},{"key":"18_CR33","unstructured":"Jung, A.: imgaug. GitHub (Accessed 14 Jan 2025). https:\/\/github.com\/aleju\/imgaug"},{"key":"18_CR34","unstructured":"Jung, A.: imgaug. readthedocs - imgaug 0.4.0 documentation (Accessed 14 Jan 2025). https:\/\/imgaug.readthedocs.io\/en\/latest\/"},{"key":"18_CR35","doi-asserted-by":"publisher","unstructured":"Montavon, G., Binder, A., Lapuschkin, S., Samek, W., M\u00fcller, KR.: Layer-wise relevance propagation: an overview. explainable AI: interpreting, explaining and visualizing deep learning. Lecture Notes in Computer Science(), 11700. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-28954-6_10","DOI":"10.1007\/978-3-030-28954-6_10"},{"key":"18_CR36","unstructured":"Stavros T: easy_explain, GitHub (Accessed 14 Jan 2025). https:\/\/github.com\/stavrostheocharis\/easy_explain"},{"key":"18_CR37","doi-asserted-by":"publisher","unstructured":"Padilla, R., Netto, S. L., Da Silva, E. A.: A survey on performance metrics for object-detection algorithms. In: 2020 international conference on systems, signals and image processing (IWSSIP), pp. 237\u2013242 (2020). https:\/\/doi.org\/10.1109\/IWSSIP48289.2020.9145130","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"key":"18_CR38","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1016\/j.procs.2021.04.009","volume":"184","author":"L Malburg","year":"2021","unstructured":"Malburg, L., Rieder, M.-P., Seiger, R., Klein, P., Bergmann, R.: Object detection for smart factory processes by machine learning. Procedia Comput. Sci. 184, 581\u2013588 (2021). https:\/\/doi.org\/10.1016\/j.procs.2021.04.009","journal-title":"Procedia Comput. Sci."}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-00071-2_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T04:35:58Z","timestamp":1757478958000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-00071-2_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783032000705","9783032000712"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-00071-2_18","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"22 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IntelliSys","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Intelligent Systems Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Amsterdam","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"intellisys12025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/saiconference.com\/IntelliSys","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}</enrichment>
    <enrichment key="opus_crossrefDocumentType">book-chapter</enrichment>
    <enrichment key="local_crossrefLicence">https://www.springernature.com/gp/researchers/text-and-data-mining</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PublisherName,PublisherPlace,TitleMain_1,Language,TitleParent_1,PageNumber,PageFirst,PageLast,PublishedYear,IdentifierIsbn,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Referiert">Beitrag ist referiert / Article peer-reviewed</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="UBICOseries">Lecture Notes in Networks and Systems ; 1567</enrichment>
    <author>
      <firstName>Toni</firstName>
      <lastName>Schneidereit</lastName>
    </author>
    <submitter>
      <firstName>Alexander</firstName>
      <lastName>Köhler</lastName>
    </submitter>
    <author>
      <firstName>Stefan</firstName>
      <lastName>Gohrenz</lastName>
    </author>
    <author>
      <firstName>Michael</firstName>
      <lastName>Breuß</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Object detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Dataset construction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Complex</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>background</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>YOLO characteristics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>YOLO heatmaps</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>factory</value>
    </subject>
    <collection role="institutes" number="1303">FG Angewandte Mathematik</collection>
  </doc>
</export-example>
