Temporary Traffic Control Device Detection for Road Construction Projects Using Deep Learning Application
Publication: Construction Research Congress 2022
ABSTRACT
Traffic control devices in road construction zones play important roles, which (1) provide critical traffic-related information for the drivers, (2) prevent potential crashes near work zones, and (3) protect work crews’ safety. Due to the number of devices in each site, transportation agencies have faced challenges in timely and frequently inspecting traffic control devices, including temporary devices. Deep learning applications can support these inspection processes. The first step of the inspection using deep learning is recognizing traffic control devices in the work zone. This study collected road images using vehicle-mounted cameras from various illuminance and weather conditions. Then, the study (1) labeled eight classes of temporary traffic control devices (TTCDs), (2) modified and trained a machine-learning model using the YOLOv3 algorithm, and (3) tested the detection outcomes of various TTCDs. The key finding shows that the proposed model recognized more than 98% of the temporary traffic signs correctly and approximately 81% of temporary traffic control devices correctly. The construction barricade had the lowest mean Average Precision (50%) out of eight classes. The outcomes can be used as the first step of autonomous safety inspections for road construction projects.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Arcos-Garcia, A., Alvarez-Garcia, J. A., and Soria-Morillo, L. M. (2018). “Evaluation of deep neural networks for traffic sign detection systems.” Neurocomputing, 316, 332–344.
Bloch, T., and Sacks, R. (2018). Comparing machine learning and rule-based inferencing for semantic enrichment of BIM models. Automation in Construction, 91, 256–272.
Cheng, J. C., Chen, W., Chen, K., and Wang, Q. (2020). “Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms.” Automation in Construction, 112, 103087.
Chae, M., Kang, K., Koo, D., Oh, S., and Chun, J. Y. (2020). “Fuzzy Controller Algorithm for Automated HVAC Control.” In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction (Vol. 37, pp. 566–570). IAARC Publications.
Kang, K., Chen, D., Peng, C., Koo, D., Kang, T., and Kim, J. (2020). “Development of an Automated Visibility Analysis Framework for Pavement Markings Based on the Deep Learning Approach.” Remote Sensing, 12(22), 3837.
Kang, T., Patil, S., Kang, K., Koo, D., and Kim, J. (2020). “Rule-based scan-to-BIM mapping pipeline in the plumbing system.” Applied Sciences (Switzerland), 10(21).
Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. Microsoft COCO: Common objects in context. In European Conference on Computer Vision; Springer: Cham, Switzerland,2014; 8693 LNCS (PART 5); pp. 740–755.
Poh, C. Q., Ubeynarayana, C. U., and Goh, Y. M. (2018). “Safety leading indicators for construction sites: A machine learning approach.” Automation in construction, 93, 375–386.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). “You only look once: Unified, real-time object detection.” In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779–788).
Stallkamp, J., Schlipsing, M., Salmen, J., and Igel, C. (2011, July). “The German traffic sign recognition benchmark: a multi-class classification competition.” In The 2011 international joint conference on neural networks (pp. 1453–1460). IEEE.
Tixier, A. J. P., Hallowell, M. R., Rajagopalan, B., and Bowman, D. (2016). “Application of machine learning to construction injury prediction.” Automation in construction, 69, 102–114.
Wu, T., and Ranganathan, A. (2012, June). “A practical system for road marking detection and recognition.” In 2012 IEEE Intelligent Vehicles Symposium (pp. 25–30). IEEE.
Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., and Hu, S. (2016). “Traffic-sign detection and classification in the wild.” In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2110–2118).
Information & Authors
Information
Published In
Construction Research Congress 2022
Pages: 392 - 401
History
Published online: Mar 7, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Chi Tian, Kyubyung Kang, Yanchao Zheng, Kwonsik Song, Luciana Debs, Feasibility of Low-Cost 3D Reconstruction of Small Infrastructure Assets: A Case Study of Fire Hydrants, Computing in Civil Engineering 2023, 10.1061/9780784485224.043, (352-360), (2024).