{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:25:01Z","timestamp":1742912701796,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031250811"},{"type":"electronic","value":"9783031250828"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-25082-8_18","type":"book-chapter","created":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T09:12:42Z","timestamp":1676106762000},"page":"268-282","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving Object Detection in\u00a0VHR Aerial Orthomosaics"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8679-6828","authenticated-orcid":false,"given":"Tanguy","family":"Ophoff","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3667-7406","authenticated-orcid":false,"given":"Kristof","family":"Van Beeck","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7477-8961","authenticated-orcid":false,"given":"Toon","family":"Goedem\u00e9","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,12]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Acatay, O., Sommer, L., Schumann, A., Beyerer, J.: Comprehensive evaluation of deep learning based detection methods for vehicle detection in aerial imagery. In: 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1\u20136. IEEE (2018)","DOI":"10.1109\/AVSS.2018.8639127"},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25rd ACM SIGKDD International Conference on Knowledge Discovery and Data Minding (2019)","DOI":"10.1145\/3292500.3330701"},{"issue":"3","key":"18_CR3","doi-asserted-by":"publisher","first-page":"458","DOI":"10.3390\/rs12030458","volume":"12","author":"U Alganci","year":"2020","unstructured":"Alganci, U., Soydas, M., Sertel, E.: Comparative research on deep learning approaches for airplane detection from very high-resolution satellite images. Remote Sens. 12(3), 458 (2020)","journal-title":"Remote Sens."},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.: YOLACT: real-time instance segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9157\u20139166 (2019)","DOI":"10.1109\/ICCV.2019.00925"},{"key":"18_CR5","doi-asserted-by":"publisher","unstructured":"Ding, J., et al.: Object detection in aerial images: a large-scale benchmark and challenges. IEEE Trans. Pattern Anal. Mach. Intell. 1 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2021.3117983","DOI":"10.1109\/TPAMI.2021.3117983"},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Eitel, A., Springenberg, J.T., Spinello, L., Riedmiller, M., Burgard, W.: Multimodal deep learning for robust RGB-D object recognition. In: 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 681\u2013687. IEEE (2015)","DOI":"10.1109\/IROS.2015.7353446"},{"key":"18_CR7","doi-asserted-by":"publisher","unstructured":"Farahnakian, F., Heikkonen, J.: A comparative study of deep learning-based RGB-depth fusion methods for object detection. In: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1475\u20131482 (2020). https:\/\/doi.org\/10.1109\/ICMLA51294.2020.00228","DOI":"10.1109\/ICMLA51294.2020.00228"},{"key":"18_CR8","doi-asserted-by":"publisher","unstructured":"Girshick, R.: Fast R-CNN. In: ICCV, pp. 1440\u20131448 (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.169","DOI":"10.1109\/ICCV.2015.169"},{"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. In: CVPR, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: 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"},{"issue":"8","key":"18_CR11","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.1109\/LGRS.2016.2565705","volume":"13","author":"Z Liu","year":"2016","unstructured":"Liu, Z., Wang, H., Weng, L., Yang, Y.: Ship rotated bounding box space for ship extraction from high-resolution optical satellite images with complex backgrounds. IEEE Geosci. Remote Sens. Lett. 13(8), 1074\u20131078 (2016)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"7","key":"18_CR12","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.3390\/rs12071217","volume":"12","author":"T Ophoff","year":"2020","unstructured":"Ophoff, T., Puttemans, S., Kalogirou, V., Robin, J.P., Goedem\u00e9, T.: Vehicle and vessel detection on satellite imagery: A comparative study on single-shot detectors. Remote Sens. 12(7), 1217 (2020)","journal-title":"Remote Sens."},{"key":"18_CR13","doi-asserted-by":"publisher","unstructured":"Ophoff, T., Van Beeck, K., Goedem\u00e9, T.: Exploring RGB+depth fusion for real-time object detection. Sensors 19(4) (2019). https:\/\/doi.org\/10.3390\/s19040866, https:\/\/www.mdpi.com\/1424-8220\/19\/4\/866","DOI":"10.3390\/s19040866"},{"key":"18_CR14","doi-asserted-by":"publisher","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: CVPR, pp. 6517\u20136525 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.690","DOI":"10.1109\/CVPR.2017.690"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"18_CR16","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. Technical report (2018)"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Schwarz, M., Schulz, H., Behnke, S.: RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 1329\u20131335. IEEE (2015)","DOI":"10.1109\/ICRA.2015.7139363"},{"key":"18_CR18","unstructured":"Van Etten, A.: You only look twice: rapid multi-scale object detection in satellite imagery. arXiv preprint arXiv:1805.09512 (2018)"},{"key":"18_CR19","doi-asserted-by":"publisher","unstructured":"Zhou, K., Paiement, A., Mirmehdi, M.: Detecting humans in RGB-D data with CNNs. In: 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), pp. 306\u2013309 (2017). https:\/\/doi.org\/10.23919\/MVA.2017.7986862","DOI":"10.23919\/MVA.2017.7986862"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25082-8_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T13:05:14Z","timestamp":1709816714000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25082-8_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250811","9783031250828"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25082-8_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"12 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","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":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"From the workshops, 367 reviewed full papers have been selected for publication","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}