This paper presents an augmented reality–based system for object and component recognition aimed at supporting product maintenance, repair, and lifecycle extension within a circular economy framework. The research focuses on bicycles as a case study, proposing a mobile application that combines 3D modeling, computer vision, and AR visualization to identify components and link them to contextualized repair information. The methodology integrates geometric analysis, average-shape modeling, and deep learning–based object recognition to enable scalable detection across different bicycle types. The workflow includes the definition of points of interest (PoIs), the creation of a generalized 3D model, and its implementation within Unity and Vuforia environments for AR interaction. As illustrated in the methodological diagram (Fig. 4, p. 613), the system connects recognition, information retrieval, and user interaction through a structured pipeline. The application allows users to visualize repair instructions, access multimedia content, and locate nearby service points, bridging digital knowledge and physical intervention. Results demonstrate the effectiveness of AR in improving component awareness, facilitating repair practices, and promoting sustainable product use, while highlighting limitations related to geometric variability and recognition accuracy across different product typologies.
Object Detection Techniques Applied to UAV Photogrammetric Survey
This project proposes an automated approach to the census of technological and architectural el-ements from massive photography datasets. This use case is built on photogrammetric close-range acquisitions performed via UAV over the roofs of the centre of Bethlehem, in order to map the water tanks for civilian use that create loads on historical buildings in a seismic area. The urban census was conducted within “3D Bethlehem. Management and control of urban growth for the development of Heritage and Improvement of life in the city of Bethlehem”, a project promoted by AICS. The pre-sented work leverages the project dataset to train Deep Learning models on a Cloud Infrastructure handling model lifecycle from training to deployment. Tests were conducted on historical buildings that show, among objects of interest, multiple spurious elements such as debris and junk. Such density creates complex scenarios for models that are trained to automate recurrent operations to assist large scale monitoring and management of the areas for different teams and municipalities.
AI+AR: Cultural Heritage, Museum Institutions, Plastic Models and Prototyping. A State of Art
The links between representation and artificial intelligence (AI) invade many fields of architectural re-search, recording continuous and significant advances: they require, on the one hand, a constant update of the state of the art and, on the other hand, careful consideration of the role of Representation in interdisciplinary research in this field. The present contribution intends to investigate these intertwining in some of the most frequented research fields in recent years: the valorization and communication of Cultural Heritage and cultural tourism, the experiences in the museum field, the research on the role of the prototype within the processes of artificial intelligence applied to architecture.