Classification of images from construction sites using a deep-learning algorithm

Field engineers take and collect several pictures from construction sites every day, and these pictures serve as records of a project. However, many of these images are loaded to and remain on computers in an unorganized manner because tagging, renaming, and organizing them is a time-consuming process. This paper proposes a method for automatically classifying construction photographs by job-type using a deep-learning algorithm. The first goal of this study is to classify construction images according to 27 job-types based on OmniClass Level 2. Google Inception v3—a deep learning algorithm used in this study as an image classifier—was trained using 1,208 construction pictures labeled by job-type. To improve the performance of the classifier, the optimized number of trainings was determined by examining the changes of accuracy and cross-entropy during trainings. The first result shows the incidence of several trainings over 50,000 was not meaningful. The retrained Google Inception as a construction image classifier was validated using a total of 235 images. The validation result shows that the classifier demonstrates an accuracy of 92.6% in classifying inputs properly and an average precision of 58.2% in correct classification. This means that retrained classifier can classify approximately nine out of every ten images correctly and that the deep-learning algorithm has high potential for use in the automatic classification of images from construction sites.