The inspection of bridge structural cracks is essential to the structural safety evaluation and could provide reference for preventive maintenance. The traditional bridge structure inspection methods rely heavily on trained engineers with professional equipment. While such kind of way could provide reliable crack inspection data, the enormous amount of existing bridges waiting for inspection challenges the efficiency of these methods. Fortunately, the development of smartphones facilitates the possibility of making the pedestrian taking smartphones a mobile sensing node, which is able to collect crack information such as images and locations. At the same time, the booming deep learning methods could offer remarkable crack detection capacity to deal with the crack images automatically. Given this consideration, this paper established a crowd-sensing-based system for bridge structural crack detection. The system was composed of the cloud-based management platform and the mobile based application (APP) for crack information collection. The mobile-based APP was used by the volunteer pedestrians to collect the crack images as well as the locations, and the location accuracy was estimated to be around 5~10 m. Meanwhile, the cloud-based management platform was used for the management of the users and the collected crack information uploaded by all of the volunteers. A deep neural network was used to deal with the crack detection tasks and evaluate the quality of the collected images to see if they could be fitted for crack detection in bridge inspection works.

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