{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T02:14:45Z","timestamp":1773713685329,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:00:00Z","timestamp":1668384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Project of China","award":["2021YFB2600603"],"award-info":[{"award-number":["2021YFB2600603"]}]},{"name":"National Key R&amp;D Project of China","award":["52208428"],"award-info":[{"award-number":["52208428"]}]},{"name":"National Key R&amp;D Project of China","award":["2242022R10060"],"award-info":[{"award-number":["2242022R10060"]}]},{"name":"National Key R&amp;D Project of China","award":["SJCX21_0071"],"award-info":[{"award-number":["SJCX21_0071"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021YFB2600603"],"award-info":[{"award-number":["2021YFB2600603"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52208428"],"award-info":[{"award-number":["52208428"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2242022R10060"],"award-info":[{"award-number":["2242022R10060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["SJCX21_0071"],"award-info":[{"award-number":["SJCX21_0071"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2021YFB2600603"],"award-info":[{"award-number":["2021YFB2600603"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["52208428"],"award-info":[{"award-number":["52208428"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2242022R10060"],"award-info":[{"award-number":["2242022R10060"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["SJCX21_0071"],"award-info":[{"award-number":["SJCX21_0071"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["2021YFB2600603"],"award-info":[{"award-number":["2021YFB2600603"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["52208428"],"award-info":[{"award-number":["52208428"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["2242022R10060"],"award-info":[{"award-number":["2242022R10060"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["SJCX21_0071"],"award-info":[{"award-number":["SJCX21_0071"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The safety of road construction is one of the most important concerns of construction managers for the following reasons: long-span construction operation, no fixed monitoring cameras, and huge impacts on existing traffic, while the managers still rely on manual inspection and a lack of image records. With the fast development of Unmanned Aerial Vehicle (UAV) and Artificial Intelligence (AI), monitoring safety concerns of road construction sites becomes easily accessible. This research aims to integrate UAVs and AI to establish a UAV-based road construction safety monitoring platform. In this study, road construction safety factors including constructors, construction vehicles, safety signs, and guardrails are defined and monitored to make up for the lack of image data at the road construction site. The main findings of this study include three aspects. First, the flight and photography schemes are proposed based on the UAV platform for information collection for road construction. Second, deep learning algorithms including YOLOv4 and DeepSORT are utilized to automatically detect and track safety factors. Third, a road construction dataset is established with 3594 images. The results show that the UAV-based monitoring platform can help managers with security inspection and recording images.<\/jats:p>","DOI":"10.3390\/s22228797","type":"journal-article","created":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T02:36:40Z","timestamp":1668479800000},"page":"8797","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Monitoring and Identification of Road Construction Safety Factors via UAV"],"prefix":"10.3390","volume":"22","author":[{"given":"Chendong","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4134-4064","authenticated-orcid":false,"given":"Junqing","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianxiang","family":"Bu","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofei","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,14]]},"reference":[{"key":"ref_1","first-page":"640","article-title":"Risk factors in a road construction site. Proceedings of the World Academy of Science","volume":"46","author":"Gannapathy","year":"2008","journal-title":"Eng. Technol."},{"key":"ref_2","first-page":"71","article-title":"Automated traffic light system for road user\u2019s safety in two lane road construction sites","volume":"2","author":"Subramaniam","year":"2010","journal-title":"WSEAS Trans. Circuits Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.trpro.2020.10.011","article-title":"Safety of transporting granular road construction materials in urban environment","volume":"50","author":"Dobromirov","year":"2020","journal-title":"Transp. Res. Procedia"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"81","DOI":"10.4236\/ojsst.2020.103007","article-title":"Investigation into Road Construction Safety Management Techniques","volume":"10","author":"Nkurunziza","year":"2020","journal-title":"Open J. Saf. Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/S0925-7535(01)00006-6","article-title":"Safety climate factors, group differences and safety behaviour in road construction","volume":"39","author":"Glendon","year":"2001","journal-title":"Saf. Sci."},{"key":"ref_6","unstructured":"Cooke, T., Lingard, H., and Blismas, N. (2008, January 9\u201311). Multi-level safety climates: An investigation into the health and safety of workgroups in road construction. Proceedings of the 14th Rinker International Conference, Gainesville, FL, USA."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zadobrischi, E., and Dimian, M. (2021). Inter-Urban Analysis of Pedestrian and Drivers through a Vehicular Network Based on Hybrid Communications Embedded in a Portable Car System and Advanced Image Processing Technologies. Remote Sens., 13.","DOI":"10.3390\/rs13071234"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"05016019","DOI":"10.1061\/(ASCE)CO.1943-7862.0001223","article-title":"Framework of automated construction-safety monitoring using cloud-enabled BIM and BLE mobile tracking sensors","volume":"143","author":"Park","year":"2017","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.autcon.2019.02.004","article-title":"Collaborative information integration for construction safety monitoring","volume":"102","author":"Xu","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103706","DOI":"10.1016\/j.autcon.2021.103706","article-title":"Rapid safety monitoring and analysis of foundation pit construction using unmanned aerial vehicle images","volume":"128","author":"Wu","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.aei.2015.02.001","article-title":"Computer vision techniques for construction safety and health monitoring","volume":"29","author":"Seo","year":"2015","journal-title":"Adv. Eng. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9915745","DOI":"10.1155\/2021\/9915745","article-title":"Research and Application of Intelligent Monitoring System Platform for Safety Risk and Risk Investigation in Urban Rail Transit Engineering Construction","volume":"2021","author":"Wu","year":"2021","journal-title":"Adv. Civ. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, W., Meng, Q., Li, Z., and Hu, X. (2021). Applications of Computer Vision in Monitoring the Unsafe Behavior of Construction Workers: Current Status and Challenges. Buildings, 11.","DOI":"10.3390\/buildings11090409"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103991","DOI":"10.1016\/j.autcon.2021.103991","article-title":"Pavement distress detection using convolutional neural networks with images captured via UAV","volume":"133","author":"Zhu","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"04022035","DOI":"10.1061\/(ASCE)CF.1943-5509.0001748","article-title":"A UAV Photography\u2013Based Detection Method for Defective Road Marking","volume":"36","author":"Bu","year":"2022","journal-title":"J. Perform. Constr. Facil."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7963","DOI":"10.1007\/s13369-022-06738-0","article-title":"Recent Advances in Unmanned Aerial Vehicles: A Review","volume":"47","author":"Ahmed","year":"2022","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yu, T., Deng, B., Gui, J., Zhu, X., and Yao, W. (2022). Efficient Informative Path Planning via Normalized Utility in Unknown Environments Exploration. Sensors, 22.","DOI":"10.3390\/s22218429"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1016\/j.comcom.2020.01.023","article-title":"Blockchain envisioned UAV networks: Challenges, solutions, and comparisons","volume":"151","author":"Mehta","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.comcom.2021.01.003","article-title":"Addressing disasters in smart cities through UAVs path planning and 5G communica-tions: A systematic review","volume":"168","author":"Qadir","year":"2021","journal-title":"Comput. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2100","DOI":"10.1109\/TITS.2020.3040557","article-title":"A Novel UAV-Enabled Data Collection Scheme for Intelligent Transportation System Through UAV Speed Control","volume":"22","author":"Li","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1109\/TITS.2016.2617202","article-title":"An Enhanced Viola-Jones Vehicle Detection Method from Unmanned Aerial Vehicles Imagery","volume":"18","author":"Xu","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1139\/cjfr-2014-0347","article-title":"A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques","volume":"45","author":"Yuan","year":"2015","journal-title":"Can. J. For. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"22641","DOI":"10.1109\/TITS.2021.3103645","article-title":"Energy-Aware Blockchain and Federated Learning-Supported Vehicular Networks","volume":"23","author":"Aloqaily","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhan, H., Liu, Y., Cui, Z., and Cheng, H. (2019, January 27\u201330). Pedestrian detection and behavior recognition based on vision. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand.","DOI":"10.1109\/ITSC.2019.8917264"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.isprsjprs.2018.04.003","article-title":"Multi-scale object detection in remote sensing imagery with convolutional neural networks","volume":"145","author":"Deng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105590","DOI":"10.1016\/j.knosys.2020.105590","article-title":"Object Detection Binary Classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance","volume":"194","author":"Tabik","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","article-title":"Deep learning for generic object detection: A survey","volume":"128","author":"Liu","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object detection with deep learning: A review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Neural Net. Learn. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/MSP.2017.2749125","article-title":"Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey","volume":"35","author":"Han","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1109\/TPAMI.2019.2956516","article-title":"Cascade R-CNN: High Quality Object Detection and Instance Segmentation","volume":"43","author":"Cai","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1109\/TIP.2018.2867198","article-title":"Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection","volume":"28","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, H., Fan, K., Ouyang, Q., and Li, N. (2021). Real-Time Small Drones Detection Based on Pruned YOLOv4. Sensors, 21.","DOI":"10.3390\/s21103374"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shi, Q., and Li, J. (2020, January 14\u201316). Objects detection of UAV for anti-UAV based on YOLOv4. Proceedings of the 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT), Weihai, China.","DOI":"10.1109\/ICCASIT50869.2020.9368788"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Parico AI, B., and Ahamed, T. (2021). Real time pear fruit detection and counting using YOLOv4 models and deep SORT. Sensors, 21.","DOI":"10.3390\/s21144803"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhang, Q., Liang, X., Wang, Y., Zhou, C., and Mikulovich, V.I. (2022). Traffic Lights Detection and Recognition Method Based on the Improved YOLOv4 Algorithm. Sensors, 22.","DOI":"10.3390\/s22010200"},{"key":"ref_36","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao HY, M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_37","first-page":"111792L","article-title":"Multi-target tracking of surveillance video with differential YOLO and DeepSort","volume":"Volume 11179","author":"Zhang","year":"2019","journal-title":"Proceedings of the Eleventh International Conference on Digital Image Processing (ICDIP 2019)"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chan, Z.Y., and Suandi, S.A. (2019, January 17\u201319). City tracker: Multiple object tracking in urban mixed traffic scenes. Proceedings of the 2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICSIPA45851.2019.8977783"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Doan, T.N., and Truong, M.T. (2020, January 12\u201314). Real-time vehicle detection and counting based on YOLO and DeepSORT. Proceedings of the 2020 12th International Conference on Knowledge and Systems Engineering (KSE), Can Tho, Vietnam.","DOI":"10.1109\/KSE50997.2020.9287483"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Rezaei, M., and Azarmi, M. (2020). DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic. Appl. Sci., 10.","DOI":"10.21203\/rs.3.rs-68650\/v1"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3190","DOI":"10.1109\/TITS.2020.3003782","article-title":"High-Resolution Vehicle Trajectory Extraction and Denoising from Aerial Videos","volume":"22","author":"Chen","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/TITS.2018.2797697","article-title":"Real-time traffic flow parameter estimation from UAV video based on ensemble classifier and optical flow","volume":"20","author":"Ke","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, J., Guo, H., Li, Z., Song, A., and Niu, X. (2022). Quantile Deep Learning Model and Multi-objective Opposition Elite Marine Predator Optimization Algorithm for Wind Speed Prediction. Appl. Math. Model., in press.","DOI":"10.1016\/j.apm.2022.10.052"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Baidya, R., and Jeong, H. (2022). YOLOv5 with ConvMixer Prediction Heads for Precise Object Detection in Drone Imagery. Sensors, 22.","DOI":"10.3390\/s22218424"},{"key":"ref_45","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wojke, N., Bewley, A., and Paulus, D. (2017, January 17\u201320). Simple online and realtime tracking with a deep association metric. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B. (2016, January 25\u201328). Simple online and realtime tracking. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"ref_48","unstructured":"Zhu, J. (2022, November 09). Road-Construction Dataset. Available online: https:\/\/github.com\/zjq2007333\/road_construction.git."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8797\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:17:53Z","timestamp":1760145473000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8797"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,14]]},"references-count":48,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22228797"],"URL":"https:\/\/doi.org\/10.3390\/s22228797","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,14]]}}}