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Data Sensing & Acquisition 1
Subtopic: Computer Vision
11:00am - 11:20am
Joint Detection And Activity Recognition Of Construction Workers Using Convolutional Neural Networks*
Concordia University, Canada
Manually gathering information about activities on construction sites for project management purposes is labor-intensive and time-consuming. As a result, several works leveraged the already installed surveillance cameras to automate this process. However, the recent learning-based methods discretize continuous activities by assigning a single label to multiple consecutive frames. They do not fully leverage the contextual cues in the scene, and are not optimized end-to-end. A variation of the YOWO network, called YOWO53, is proposed in this paper to address these limitations. YOWO53 shows better classification and detection results over YOWO and allows using smaller input frames with real-time speed.
External Resource: https://ec-3.org/publications/conferences/2021/paper/?id=197
11:20am - 11:40am
Efficient Vertical Object Detection in Large High-Quality Point Clouds of Construction Sites*
1Chair of Computational Modeling and Simulation, Technical University of Munich, Germany; 2FARO EUROPE GmbH & Co.KG., Korntal-Münchingen, Germany
Even when adherence to the project schedule is a critical performance metric, still 53% of construction projects exhibit schedule delays. To contribute to efficient construction progress monitoring, a method is proposed to detect temporary objects in scans of construction sites. The proposed workflow includes: image processing, computer vision, and deep learning techniques. The method was tested on three real scans and with three object categories (cranes, scaffolds, and formwork). It achieved average rates above 88% for precision and recall and outstanding computational performance (1s to process 10^5points). These metrics demonstrate the method’s capability to segment point clouds of construction sites.
External Resource: https://ec-3.org/publications/conferences/2021/paper/?id=156
11:40am - 12:00pm
Towards real-time Scan-versus-BIM :methods applications and challenges
1Centre for Systems Engineering and Innovation, Imperial College London, United Kingdom; 2The Alan Turing Institute, United Kingdom
There has been much work on Scan-vs-BIM, and it has implications on construction productivity, quality, and safety. However, these methods need to be extended /altered to do real-time Scan-vs-BIM. This paper presents the extensions for existing methods such as registration, point matching, object detection, and pose estimation to cater to real-time Scan-vs-BIM. Further, we describe the applications for such methods in construction and de-scribes the challenges for their implementation. We con-clude by implication of this paper on researchers working on augmented reality and construction robotics.
External Resource: https://ec-3.org/publications/conferences/2021/paper/?id=176
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