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Albeaino, G, Brophy, P, Jeelani, I, Gheisari, M and Issa, R R A (2023) Impact of drone presence on construction individuals working at heights. Journal of Construction Engineering and Management, 149(11).
Chadee, A A, Martin, H, Chadee, X T, Bahadoorsingh, S and Olutoge, F (2023) Root cause of cost overrun risks in public sector social housing programs in sids: Fuzzy synthetic evaluation. Journal of Construction Engineering and Management, 149(11).
Duan, P, Zhou, J and Goh, Y M (2023) Safety risk diagnosis based on motion trajectory for construction workers: An integrated approach. Journal of Construction Engineering and Management, 149(11).
Erk, E Y, Budayan, C, Koc, K and Tokdemir, O B (2023) Value creation in PPP projects undertaken in the Turkish healthcare industry. Journal of Construction Engineering and Management, 149(11).
Hsu, C L, Wang, J T and Hou, H Y (2023) A blockchain-based parametric model library for knowledge sharing in building information modeling collaboration. Journal of Construction Engineering and Management, 149(11).
Lim, H W and Francis, V (2023) A conceptual model of cognitive and behavioral processes affecting mental health in the construction industry: A systematic review. Journal of Construction Engineering and Management, 149(11).
Mostofi, F, Toǧan, V, Başaǧa, H B, Çltlpltloǧlu, A and Tokdemir, O B (2023) Multiedge graph convolutional network for house price prediction. Journal of Construction Engineering and Management, 149(11).
Tang, Y and Yao, H (2023) Watch out for the hidden costs of subcontracting in construction projects: The impacts of subcontractor dispersion. Journal of Construction Engineering and Management, 149(11).
Wang, D, Huang, R, Qiao, Y, Sheng, Z, Li, K and Zhao, L (2023) How perceived leader-member exchange differentiation affects construction workers' safety citizenship behavior: Organizational identity and felt safety responsibility as mediators. Journal of Construction Engineering and Management, 149(11).
Wang, S, Kim, M, Hae, H, Cao, M and Kim, J (2023) The development of a rebar-counting model for reinforced concrete columns: Using an unmanned aerial vehicle and deep-learning approach. Journal of Construction Engineering and Management, 149(11).
Wang, Z, He, Q, Locatelli, G, Wang, G and Li, Y (2023) Exploring environmental collaboration and greenwashing in construction projects: Integrative governance framework. Journal of Construction Engineering and Management, 149(11).
Watton, J, Unterhitzenberger, C, Locatelli, G and Invernizzi, D C (2023) The cost drivers of infrastructure projects: Definition, classification, and conceptualization. Journal of Construction Engineering and Management, 149(11).
Wu, H, Han, Y, Zhang, M, Abebe, B D, Legesse, M B and Jin, R (2023) Identifying unsafe behavior of construction workers: A dynamic approach combining skeleton information and spatiotemporal features. Journal of Construction Engineering and Management, 149(11).
Wu, L, Mohamed, E, Jafari, P and Abourizk, S (2023) Machine learning-based Bayesian framework for interval estimate of unsafe-event prediction in construction. Journal of Construction Engineering and Management, 149(11).
Wu, S, Yu, L, Cao, T, Yuan, C and Du, Y (2023) How dependence asymmetry and explicit contract shape contractor-subcontractor collaboration: A psychological perspective of fairness. Journal of Construction Engineering and Management, 149(11).
You, H, Xu, F and Du, J (2023) Improved boundary identification of stacked objects with sparse lidar augmentation scanning. Journal of Construction Engineering and Management, 149(11).
- Type: Journal Article
- Keywords: environmental understanding; light detection and ranging scanning; object detection
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-13626
- Abstract:
Vision-based sensors have been widely used in reality capture and the corresponding scene understanding tasks such as object detection. Given the increasing complexity of built environments, geometric features from the raw scanning data can become too vague for effective object detection. One example challenge is stacked object recognition, i.e., the segmentation, detection, and recognition of objects being stacked together with similar geometric features or occlusions. Previous methods propose to use high-resolution sensors to capture more detailed geometry information to highlight the boundaries between adjacent objects, which increase the deployment cost and computing needs. This paper proposes a novel data augmentation and voting method for stacked object detection with only low-cost sparse sensors. Several locomotion strategies were used to focus on filling the gaps of the sparse light detection and ranging (LiDAR) sensor. A modified LiDAR odometry and mapping (LOAM) method was used to register and augment raw point cloud data from multiple scans in real time. Then a voxel-based density voting method was applied to centralize the points in enhanced scan for a more accurate clustering. Finally, the clustered points were grouped and applied to generate three-dimensional (3D) bounding boxes for object boundary identification. A pilot test was performed to show the improved results of the proposed methods. A series of benchmarking studies were also performed to identify the minimum acceptable density level for the proposed method.
Zheng, X, Chen, J, Xia, B, Skitmore, M and Zeng, S (2023) Understanding the megaproject social responsibility network among stakeholders: A reciprocal-exchange perspective. Journal of Construction Engineering and Management, 149(11).
Zhou, Q, Deng, X, Hwang, B G, Mahmoudi, A and Liu, Y (2023) Integrating the factors affecting knowledge transfer within international construction projects: Individual and team perspectives. Journal of Construction Engineering and Management, 149(11).