Low-cost ios-based automated detection of under-construction interior drywalls: An exploratory study

As an emerging direction in smart construction, automated interior construction progress monitoring (ICPM) provides more efficient progress and schedule management than traditional approaches. Technologies such as digital cameras and laser scanners have become the primary means of collecting as-built site data for ICPM applications. However, the high prices and operational complexities of these tools could discourage some construction companies from using automated ICPM methods. This study aims to explore the feasibility of a low-cost and user-friendly structure detection method, using mobile phone-based laser scanner for acquiring site data and computer vision (CV) techniques for detecting three drywall construction phases (framing, insulation, and plastered). Distinctively, our approach innovates by capitalizing on the geometric information of point cloud models rather than the traditional reliance on RGB color data, resulting in a significant acceleration of data processing and detection capabilities. Although shortcomings such as limited amount of available data and low resolution of captured data may affect the training and detection results, the outcome shows similar accuracy rates to other CV-based drywall detection approaches, indicating its potential to provide promising results for construction element detection. The study supports the feasibility of integrating iPhone Operating System (iOS) built-in LiDAR scanner for construction element detection and has also built a benchmark data set for interior drywall detection. The outcome addresses the significant barriers of high costs and complex operational demands associated with conventional digital cameras and laser scanners, proposing a readily accessible alternative that could democratize the adoption of automated ICPM practices. The benchmark data set established in this study also offers a readily accessible reference for validating new algorithms, thus accelerating innovation in low-cost, operationally simplified ICPM methods.