Abstract
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.
Original language | English |
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Publication status | Published - 2018 Jan 1 |
Event | 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, ISARC 2018 - Berlin, Germany Duration: 2018 Jul 20 → 2018 Jul 25 |
Other
Other | 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, ISARC 2018 |
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Country/Territory | Germany |
City | Berlin |
Period | 18/7/20 → 18/7/25 |
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Artificial Intelligence
- Building and Construction