Abstract
In the construction field, Building Information Modeling (BIM) offers various application scenarios in the design, construction, and maintenance phases of a building’s lifecycle. The utilization of BIM in the Operation and Maintenance (O&M) phase of existing buildings is a particular challenge, as suitable BIM models are usually not available as a basis. In this context, many researchers have investigated various methods to automatically create BIM models from existing information and data. Focusing on images, numerous methods have been investigated for detecting and classifying certain objects in buildings. However, despite the importance of fire protection in buildings, the research field has not focused on the benefits of the automatic recognition of fire safety equipment (FSE, e.g., fire blankets) in much detail. Particularly in existing buildings, the recurring inspection and maintenance of fire safety equipment is a responsible task and required by law. It is the responsibility of the owners and facility managers to ensure the availability and proper functioning of fire safety equipment in the building.
Consequently, this work aims to contribute to this research area by investigating the state-of-the-art Mask Region-Based Convolutional Neural Network (Mask R-CNN) for instance segmentation and object detection of FSE in RGB images. The results show that this approach automatically extracts valuable semantic information that provides the presence of fire safety equipment in images. In addition, this study investigates the influence of hyperparameter adjustment on the detection of FSE objects in indoor scenes. It is also examined how the additional use of augmented data improves the performance of the neural network.
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References
Ying, H., Lee, S.: A Mask R-CNN based approach to automatically construct as is IFC BIM objects from digital images. In: 36th International Symposium on Automation and Robotics in Construction (ISARC), pp. 764–771. IAARC (2019). https://doi.org/10.22260/ISARC2019/0103
Lin, T.-Y., et al.: Microsoft COCO: Common Objects in Context. http://arxiv.org/pdf/1405.0312v3 (2014). Accessed 21 Sep 2022
Boehm, J., Panella, F., Melatti, V.: FireNet (2019). Accessed 21 Sept 2022
Vandecasteele F, Merci B, Verstockt S (2017) Fireground location understanding by semantic linking of visual objects and building information models. Fire Saf J 91:1026–1034. https://doi.org/10.1016/j.firesaf.2017.03.083
Khan N, Ali AK, VanTienTran S, Lee D, Park C (2020) Visual language-aided construction fire safety planning approach in building information modeling. Appl Sci 10:1704. https://doi.org/10.3390/app10051704
Sergi I, Malagnino A, Rosito RC, Lacasa V, Corallo A, Patrono L (2020) Integrating BIM and IoT technologies in innovative fire management systems. In: 5th International Conference on Smart and Sustainable Technologies (SpliTech). IEEE. https://doi.org/10.23919/SpliTech49282.2020.9243838
Adán A, Quintana B, Prieto SA, Bosché F (2018) Scan-to-BIM for ‘secondary’ building components. Adv Eng Inform 37:119–138. https://doi.org/10.1016/j.aei.2018.05.001
Adan A, Quintana B, Prieto SA (2018) Recognition and positioning of SBCs in BIM models using a geometric vs colour consensus approach. In: 34th International Symposium on Automation and Robotics in Construction (ISARC), pp. 1–8. IAARC. https://doi.org/10.22260/ISARC2018/0122
Corneli A, Naticchia B, Vaccarini M, Bosché F, Carbonari A (2020) Training of YOLO neural network for the detection of fire emergency assets. In: 37th International Symposium on Automation and Robotics in Construction (ISARC), pp. 836–843. IAARC. https://doi.org/10.22260/ISARC2020/0115
Ferguson M, Law K (2019) A 2D-3D object detection system for updating building information models with mobile robots. In: 2019 IEEE (WACV), pp. 1357–1365
Eitel A, Hauff N, Burgard W (2019) Self-supervised transfer learning for instance segmentation through physical interaction. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4020–4026
Du G, Wang K, Lian S, Zhao K (2021) Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review. In: Artificial Intelligence Review, vol. 54, pp. 1677–1734
Xu C., et al (2020) Fast vehicle and pedestrian detection using improved mask R-CNN. In: Mathematical Problems in Engineering, vol. 2020, pp. 1–15
Raoofi H, Motamedi A (2020) Mask R-CNN deep learning-based approach to detect construction machinery on jobsites. In: 37th International Symposium on Automation and Robotics in Construction (ISARC), pp. 1122–1127. IAARC
He K, Gkioxari G, Dollar P, Girshick R (2017) Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969
Girshick R (2015) Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision. IEEE. https://doi.org/10.1109/iccv.2015.169
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In: Advances in neural information processing systems, vol. 28 (2015)
Russakovsky O (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 14:197. https://doi.org/10.1007/s11263-015-0816-y
Dutta A, Zisserman A (2019) The VIA annotation software for images, audio and video. In: The VIA Annotation Software for Images, Audio and Video, pp. 2276–2279. ACM. https://doi.org/10.1145/3343031.3350535
Waleed, A.: Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow (2017)
Padilla R, Passos WL, Dias TLB, Netto SL, Da Silva EAB (2021) A comparative analysis of object detection metrics with a companion open-source toolkit. Electronics 10:279. https://doi.org/10.3390/electronics10030279
Acknowledgements
The authors would like to thank the German Federal Ministry for Economic Affairs and Climate Action (BMWK) for the financial support provided by the BIMKIT research project.
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Aziz, A., König, M., Zengraf, S., Schulz, JU. (2024). Instance Segmentation of Fire Safety Equipment Using Mask R-CNN. In: Skatulla, S., Beushausen, H. (eds) Advances in Information Technology in Civil and Building Engineering. ICCCBE 2022. Lecture Notes in Civil Engineering, vol 357. Springer, Cham. https://doi.org/10.1007/978-3-031-35399-4_10
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