Generalized Vision-Based Detection, Identification and Pose Estimation of Lamps for BIM Integration

Abstract

This paper introduces a comprehensive approach based on computer vision for the automatic detection, identification and pose estimation of lamps in a building using the image and location data from low-cost sensors, allowing the incorporation into the building information modelling (BIM). The procedure is based on our previous work, but the algorithms are substantially improved by generalizing the detection to any light surface type, including polygonal and circular shapes, and refining the BIM integration. We validate the complete methodology with a case study at the Mining and Energy Engineering School and achieve reliable results, increasing the successful real-time processing detections while using low computational resources, leading to an accurate, cost-effective and advanced method. The suitability and the adequacy of the method are proved and concluded.

Orientation-Constrained System for Lamp Detection in Buildings Based on Computer Vision

“…The additional filtering had a very small impact on the total scores, while the use of the orientation alignment greatly increased the values. As previously mentioned, this is important to guarantee good results in the final clusters, and is specially relevant in some clusters, particularly for case studies 4 and 5, that had low values with the unconstrained system [31].…”

“…With Equation (7), and following the same procedure as the one presented in [31], we solved the minimization problem based on the distance to the circumference of radius RC to obtain the optimal values of bold-italicpC and bold-italicnfalse^. This is presented in Equations (8) and (9):truetrueprefixminbold-italicpC(L),n(L)∑i()pc(L)+truen^(L)(bold-italicpC(L)−bold-italicpc(L))truen^(L)bold-italicf(L)bold-italicf(L)−pC(L)−RC2 s.t.1.em∥<...>…”

“…The 1920 × 1080 images were extracted using a Lenovo Phab 2 Pro, with the Google Tango technology [34]. This acquisition protocol was the same as the one described in [31].…”

“…Percentages of correct and incorrect identifications are included in the last row and column. ( a ) Unconstrained system [31], ( b ) system with reprojection error filtering, and ( c ) system with filtering and orientation alignment.…”

Application of Building Information Modelling (BIM) in the Health Monitoring and Maintenance Process: A Systematic Review

“…Structural safety is the main goal of engineers all over the world; therefore, they are drawn to investigate novel ways of assessing structural defects, facilitated by BIM. Existing research in this area has focused on: goals such as conflict detection based on the life cycle versus as-built documents by considering all changes in the structure, fault detection of energy-consuming tools in buildings through BIM and simulation tools for energy monitoring, fault detection in buildings and non-structural components, damage detection in timber structures, tunnel flaws diagnosis to find maintenance strategies that are also used for utility tunnels, fault detection of hospitals for rehabilitation, automatic detection, and identification of lamps in a building [ 138 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 ]. Some papers used laser scanner data of existing structures along with BIM models [ 166 ].…”