{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:43:21Z","timestamp":1758588201493,"version":"3.44.0"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811674754"},{"type":"electronic","value":"9789811674761"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-981-16-7476-1_11","type":"book-chapter","created":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T11:09:50Z","timestamp":1635592190000},"page":"110-121","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["High-Altitude Pedestrian Detection Based on Improved YOLOv3"],"prefix":"10.1007","author":[{"given":"Qing","family":"Tian","sequence":"first","affiliation":[]},{"given":"Pengfei","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Haoyi","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Rong","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Zuoyong","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,31]]},"reference":[{"key":"11_CR1","unstructured":"Zou, Z., Shi, Z., Guo, Y., et al.: Object detection in 20 years: a survey. arXiv preprint arXiv:1905.05055 (2019)"},{"issue":"6","key":"11_CR2","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2015","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards teal-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936\u2013944 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"11_CR4","unstructured":"Yim, J. G., Lee, G.Y., Lee, T.G.: Improving the efficiency of the FPN. In: International Conference on Electronics, Informations and Communications, pp. 20\u201323 (1995)"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"11_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"11_CR7","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.procs.2019.08.147","volume":"157","author":"A Michele","year":"2019","unstructured":"Michele, A., Colin, V., Santika, D.D.: Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Comput. Sci. 157, 110\u2013117 (2019)","journal-title":"Procedia Comput. Sci."},{"issue":"4","key":"11_CR8","doi-asserted-by":"publisher","first-page":"446","DOI":"10.3390\/s16040446","volume":"16","author":"Y Ma","year":"2016","unstructured":"Ma, Y., Wu, X., Yu, G., Xu, Y., Wang, Y.: Pedestrian detection and tracking from low-resolution unmanned aerial vehicle thermal imagery. Sensors. 16(4), 446 (2016)","journal-title":"Sensors."},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO 9000: better, faster, stronger. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517\u20136525 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"11_CR10","unstructured":"Redmon, J., Ali, F., YOLOv3: an incremental improvement. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1\u20136 (2018)"},{"key":"11_CR11","unstructured":"Howard, A.G., et al.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. ArXiv abs\/1704.04861 (2017)"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Zhou, D., Fang, J., Song, X., et al.: Iou loss for 2d\/3d object detection. In: International Conference on 3D Vision (3DV), pp. 85\u201394 (2019)","DOI":"10.1109\/3DV.2019.00019"},{"key":"11_CR13","unstructured":"LeCun, Y.: LeNet-5, convolutional neural networks. http:\/\/yann.lecun.com\/exdb\/lenet20.5:14 (2015)"},{"key":"11_CR14","unstructured":"Zheng, Z., Wang, P., Liu, W., et al.: Distance-IoU Loss: faster and better learning for bounding box regression. arXiv preprint arXiv: 2019: 08287."},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Fan, H., Zhang, F., Li, Z.: AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5685\u20135689 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053387"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Li, J., Liang, X., Wei, Y., et al.: Perceptual generative adversarial networks for small object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1222\u20131230 (2017)","DOI":"10.1109\/CVPR.2017.211"},{"key":"11_CR17","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Neural Inf. Process. Syst. (NIPS) 25, 1097\u20131105 (2012)"},{"key":"11_CR18","unstructured":"Wang, R.J., Li, X., Ling, C.X.: Pelee: A real-time object detection system on mobile devices. arXiv preprint arXiv: 1804.06882 (2018)"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"De Ruvo, P., Distante, A., Stella, E., Marino, F.: A GPU-based vision system for real time detection of fastening elements in railway inspection. In: 16th IEEE International Conference on Image Processing (ICIP), pp. 2333\u20132336 (2009)","DOI":"10.1109\/ICIP.2009.5414438"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Duan, K., et al.: Centernet: keypoint triplets for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00667"}],"container-title":["Communications in Computer and Information Science","Data Mining and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-7476-1_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T22:03:30Z","timestamp":1758578610000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-7476-1_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811674754","9789811674761"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-7476-1_11","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"31 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DMBD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Data Mining and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dmbd2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/dmbd2021\/index.html","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"258","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":"57","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":"28","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":"22% - 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":"2.5","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":"8","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)"}}]}}