Image dataset development for measuring construction equipment recognition performance

Abstract

Abstract The recognition of construction operational resources (equipment, workers, materials, etc.) has played an important role in achieving fully automated construction. So far, many object recognition methods have been developed in computer vision; however, they have been tested with a few categories of objects in natural scenes. Therefore, their performance on the recognition of construction operational resources is unclear, especially considering construction sites are typically dirty, disorderly, and cluttered. This paper proposes a standard dataset of construction site images to measure the construction equipment recognition performance of existing object recognition methods. Thousands of images have been collected and compiled, which cover 5 classes of construction equipment (excavator, loader, dozer, roller and backhoe). Each image has been annotated with the equipment type, location, orientation, occlusion, and labeling of equipment components (bucket, stick, boom etc.). The effectiveness of the dataset has been evaluated with two well-known object recognition methods in computer vision. The results show that the dataset could successfully identify the performance of these methods in terms of correctness, robustness, and speed of recognizing construction equipment.