Error-correction methods for construction site image processing under changing illumination conditions

Image processing is an effective tool for automated monitoring of construction projects. For the last decade, it has gained increasing acceptance in its application to progress monitoring, productivity analyses, and quality assurance. However, a notable downside exists in image processing, especially in outdoor applications such as construction project monitoring: image quality is heavily affected by ambient lighting conditions. Poor or undesirable lighting conditions produce substandard quality images, which generally lead to a high level of errors in the related image processing for information extraction. This paper presents error-correction methods that can improve the image processing results for construction progress monitoring in the postprocessing stage. The methods are applied in the postprocessing stage. The key idea behind the error-correction methods is the concept of priority to classify input images and project information into several categories based on data reliability and intelligently use the classified information for more accurate analyses of the project progress. Tests in real construction sites showed that these postprocessing methods significantly increased the accuracy of image processing-based construction progress monitoring.