Automatic Object Detection from Digital Images by Deep Learning with Transfer Learning

At construction sites and disaster areas, an enormous number of digital photographs are taken by engineers. Tasks such as collecting, sorting, annotating, storing, deleting, distributing these digital images, as done manually, are cumbersome, error-prone, and time-consuming. Thus, it is desirable to automate the object detection process of pictures so that engineers do not have to waste their valuable time and can improve the efficiency and accuracy. Although conventional machine learning could be a solution, it takes much time for researchers to determine features and contents of digital images, and the accuracy tends to be unsatisfactory. On the other hand, deep learning can automatically determine features and contents of various objects from digital images. Therefore, this research aims to automatically detect each object as an object and its position from digital images by using deep learning. Since deep learning usually requires a very large amount of dataset, this research has adopted deep learning with transfer learning, which enables object detection even if the dataset is not very large. Experiments were executed to detect construction machines, workers, and signboards in photographs, comparing among the conventional machine learning by feature values, deep learning with and without transfer learning. The result showed that the best performance was achieved by the deep learning with transfer learning.