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In recent years, a lot of attention has been paid to deep learning methods in the context of vision-based construction site safety systems. However, there is still more to be done to establish the relationship between supervised construction workers and their essential personal protective equipment, like hard hats. A deep learning method combining object detection, head center localization, and simple rulebased reasoning is proposed in this article. In tests, this solution surpassed the previous methods based on the relative bounding box position of different instances and direct detection of hard hat wearers and non-wearers. Achieving MS COCO style overall AP of 67.5% compared to 66.4% and 66.3% achieved by the approaches mentioned above, with class-specific AP for hard hat non-wearers of 64.1% compared to 63.0% and 60.3%. The results show that using deep learning methods with a humanly interpretable rule-based algorithm is better suited for detecting hard hat non-wearers. [ABSTRACT FROM AUTHOR]

Bridge inspections are a vital part of bridge maintenance and the main information source for Bridge Management Systems is used in decision-making regarding repairs. Without a doubt, both can benefit from the implementation of the Building Information Modelling philosophy. To fully harness the BIM potential in this area, we have to develop tools that will provide inspection accurate information easily and fast. In this paper, we present an example of how such a tool can utilise tablets coupled with the latest generation RGB-D cameras for data acquisition; how these data can be processed to extract the defect surface area and create a 3D representation, and finally embed this information into the BIM model. Additionally, the study of depth sensor accuracy is presented along with surface area accuracy tests and an exemplary inspection of a bridge pillar column. [ABSTRACT FROM AUTHOR]