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Measuring onsite productivity has been a substance of debate in the construction industry, mainly due to concerns about accuracy, repeatability and unbiasedness. Such characteristics are central to demonstrate construction speed that can be achieved through adopting new prefabricated systems. Existing productivity measurement methods, however, cannot cost-effectively provide solid and replicable evidence of prefabrication benefits. This research proposes a low-cost automated method for measuring onsite installation productivity of prefabricated systems.

Firstly, the captured ultra-wide footages are undistorted by extracting the curvature contours and performing a developed meta-heuristic algorithm to straighten these contours. Then a preprocessing algorithm is developed that could automatically detect and remove the noises caused by vibrations and movements. Because this study aims to accurately measure the productivity the noise free images are double checked in a specific time window to make sure that even a tiny error, which have not been detected in the previous steps, will not been amplified through the process. In the next step, the existing side view provided by the camera is converted to a top view by using a spatial transformation method. Finally, the processed images are compared with the site drawings in order to detect the construction process over time and report the measured productivity.

The developed algorithms perform nearly real-time productivity computations through exact matching of actual installation process and digital design layout. The accuracy and noninterpretive use of the proposed method is demonstrated in construction of a multistorey cross-laminated timber building.

This study uses footages of an already installed surveillance camera where the camera's features are unknown and then image processing algorithms are deployed to retrieve accurate installation quantities and cycle times. The algorithms are almost generalized and versatile to be adjusted to measure installation productivity of other prefabricated building systems.

The purpose of this research is to develop an automatic optical inspection system for thin film transistor (TFT) liquid crystal display (LCD).

A new algorithm that accounts for the closing, opening, etching, dilating, and genetic method is used. It helps to calculate the location and rotation angle for transistor patterns precisely and quickly. The system can adjust inspection platform parameters according to viewed performance. The parameter adaptation occurs in parallel with running the genetic algorithm and imaging processing methods. The proposed method is compared with the algorithms that use artificial parameter sets.

This system ensures high quality in an LCD production line. This multipurpose image‐based measurement method uses unsophisticated and economical equipment, and it also detects defects in the micro‐fabrication process.

The experiment's results show that the proposed method offers advantages over other competing methods.