Small Tool Image Database and Object Detection Approach for Indoor Construction Site Safety

Abstract

Object detection refers to computer vision technology that identifies the class and location of objects in an image. With the development of deep-learning techniques, object detection has been widely studied in the construction industry and has mainly focused on detecting heavy equipment and workers at outdoor sites. Therefore, an object detection model for small tools in indoor sites, where various accidents frequently occur owing to them, is required to enhance safety. In particular, many high-quality image databases are important for developing object detection approaches for small tools. In this study, 12 small tools commonly used in indoor construction were selected, and a database of 34,738 images of the selected tools was created and used to train the object detection models. YOLOv5x and YOLOv5s are selected as object detection models, for which the mean average precision with the 0.5 intersection-over-union value were 69.1% and 64.4%, respectively, and their frame rates were 58.82 and 142.86 frame per second, respectively. The results demonstrate that the established database is appropriate for the development of object detection for small tools and that object detection can be used for real-time safety management of construction indoor sites.