There have been several recent image-based recognition competitions (such as the PASCAL VOC, ImageNet, and COCO challenges) based on natural objects and scenes. The time is ripe to hold additional competitions in other areas to develop interdisciplinary interactions with computer vision and Machine Learning/Deep Learning technologies.

As a pioneer study, the Pacific Earthquake Engineering Research (PEER) Center will organize the first image-based structural damage recognition competition, namely PEER Hub ImageNet (PHI) Challenge, in the summer of 2018. In the PHI Challenge, PEER will provide a large image dataset which is relevant to the field of structural engineering, and will design several detection tasks , which will contribute to the establishment of automated vision based structural health monitoring. The goal of the PHI challenge is to evaluate algorithms for structural image classification using a large-scale dataset based on regular conditions and past reconnaissance efforts after extreme events; the state-of-the-art algorithms are expected to have both accuracy and generalization towards a complex structural image dataset.