{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T09:47:59Z","timestamp":1749548879492,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819958436"},{"type":"electronic","value":"9789819958443"}],"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\/978-981-99-5844-3_40","type":"book-chapter","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T22:02:16Z","timestamp":1693432936000},"page":"546-560","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Accurate Detection of\u00a0the\u00a0Workers and\u00a0Machinery in\u00a0Construction Sites Considering the\u00a0Occlusions"],"prefix":"10.1007","author":[{"given":"Qian","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengdong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,31]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","unstructured":"Guo, Y., Cui, H., Li, S.: Excavator joint node-based pose estimation using lightweight fully convolutional network. Autom. Constr. 141, 104435 (2022)","DOI":"10.1016\/j.autcon.2022.104435"},{"key":"40_CR2","doi-asserted-by":"crossref","unstructured":"Wang, D., et al.: Vision-based productivity analysis of cable crane transportation using augmented reality\u2013based synthetic image. J. Comput. Civ. Eng. 36(1), 04021030 (2022)","DOI":"10.1061\/(ASCE)CP.1943-5487.0000994"},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Assadzadeh, A., Arashpour, M., Li, H., Hosseini, R., Elghaish, F., Baduge, S.: Excavator 3D pose estimation using deep learning and hybrid datasets. Adv. Eng. Inf. 55, 101875 (2023)","DOI":"10.1016\/j.aei.2023.101875"},{"key":"40_CR4","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xiao, B., Bouferguene, A., Al-Hussein, M., Li, H.: Vision-based method for semantic information extraction in construction by integrating deep learning object detection and image captioning. Adv. Eng. Inf. 53, 101699 (2022)","DOI":"10.1016\/j.aei.2022.101699"},{"key":"40_CR5","doi-asserted-by":"crossref","unstructured":"Chen, C., Gu, H., Lian, S., Zhao, Y., Xiao, B.: Investigation of edge computing in computer vision-based construction resource detection. Buildings 12(12), 2167 (2022)","DOI":"10.3390\/buildings12122167"},{"key":"40_CR6","doi-asserted-by":"crossref","unstructured":"Kong, T., Fang, W., Love, P.E., Luo, H., Xu, S., Li, H.: Computer vision and long short-term memory: learning to predict unsafe behaviour in construction. Adv. Eng. Inf. 50, 101400 (2021)","DOI":"10.1016\/j.aei.2021.101400"},{"key":"40_CR7","doi-asserted-by":"crossref","unstructured":"Zhai, P., Wang, J., Zhang, L.: Extracting worker unsafe behaviors from construction images using image captioning with deep learning\u2013based attention mechanism. J. Constr. Eng. Manag. 149(2), 04022164 (2023)","DOI":"10.1061\/JCEMD4.COENG-12096"},{"key":"40_CR8","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Zhao, J., Wu, W., Wen, C., Cao, Y.: Automatic object detection of construction workers and machinery based on improved YOLOv5. In: Proceedings of the 2022 International Conference on Green Building, Civil Engineering and Smart City, pp. 741\u2013749 (2022)","DOI":"10.1007\/978-981-19-5217-3_74"},{"key":"40_CR9","doi-asserted-by":"publisher","unstructured":"Wang, H., Song, Y., Huo, L., et al.: Multiscale object detection based on channel and data enhancement at construction sites. Multimedia Syst. 29, 49\u201358 (2023). https:\/\/doi.org\/10.1007\/s00530-022-00983-x","DOI":"10.1007\/s00530-022-00983-x"},{"key":"40_CR10","doi-asserted-by":"publisher","unstructured":"Guo, Y., Xu, Y., Li, Z., et al.: Enclosing contour tracking of highway construction equipment based on orientation-aware bounding box using UAV. J. Infrastruct. Preserv. Resil. 4, 4 (2023). https:\/\/doi.org\/10.1186\/s43065-023-00071-y","DOI":"10.1186\/s43065-023-00071-y"},{"key":"40_CR11","doi-asserted-by":"crossref","unstructured":"Xiao, B., Lin, Q., Chen, Y.: A vision-based method for automatic tracking of construction machines at nighttime based on deep learning illumination enhancement. Autom. Construct. 127, 103721 (2021)","DOI":"10.1016\/j.autcon.2021.103721"},{"key":"40_CR12","doi-asserted-by":"crossref","unstructured":"Chian, E., Fang, W., Goh, Y.M., Tian, J.: Computer vision approaches for detecting missing barricades. Autom. Construct. 131, 103862 (2021)","DOI":"10.1016\/j.autcon.2021.103862"},{"key":"40_CR13","doi-asserted-by":"crossref","unstructured":"Wang, X., Wang, H., Zhang, C., He, Q., Huo, L.: A sample balance-based regression module for object detection in construction sites. Appl. Sci. 12(13), 6752 (2022)","DOI":"10.3390\/app12136752"},{"key":"40_CR14","doi-asserted-by":"crossref","unstructured":"Chen, H., Hou, L., Zhang, G. K., Wu, S.: Using context-guided data augmentation, lightweight CNN, and proximity detection techniques to improve site safety monitoring under occlusion conditions. Saf. Sci. 158, 105958 (2023)","DOI":"10.1016\/j.ssci.2022.105958"},{"key":"40_CR15","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: vnified, 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":"40_CR16","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":"40_CR17","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767(2018)"},{"key":"40_CR18","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":"40_CR19","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464\u20137475 (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"40_CR20","doi-asserted-by":"crossref","unstructured":"Janardan, R., Lopez, M.: Generalized intersection searching problems. Int. J. Comput. Geom. Appl. 3(01), 39\u201369 (1993)","DOI":"10.1142\/S021819599300004X"},{"key":"40_CR21","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-IoU loss: faster and better learning for bounding box regression. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, No. 07, pp. 12993\u201313000 (2020)","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"40_CR22","doi-asserted-by":"crossref","unstructured":"Xuehui, A., Li, Z., Zuguang, L., Chengzhi, W., Pengfei, L., Zhiwei, L.: Dataset and benchmark for detecting moving objects in construction sites. Autom. Construct. 122, 103482 (2021)","DOI":"10.1016\/j.autcon.2020.103482"},{"key":"40_CR23","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"}],"container-title":["Communications in Computer and Information Science","International Conference on Neural Computing for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-5844-3_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T22:06:13Z","timestamp":1693433173000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-5844-3_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819958436","9789819958443"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-5844-3_40","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NCAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Computing for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hefei","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ncaa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dl2link.com\/ncaa2023\/","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":"Easy chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"211","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":"83","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":"1","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":"39% - 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":"3.67","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)"}}]}}