# Orthofacade-Based Assisted Inspection Method for Buildings  
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Faculty of Technical Sciences, University of Novi Sad, Trg [PERSON] 6, 21000 Novi Sad, Serbia; [EMAIL_ADDRESS] (S.D. ); [EMAIL_ADDRESS] (S.P. ); [EMAIL_ADDRESS] (M.Z.M. ); [EMAIL_ADDRESS] (LL. ); [EMAIL_ADDRESS] (M.M. ); [EMAIL_ADDRESS] (V.R.) Correspondence: [EMAIL_ADDRESS]; Tel. : +381-64-845-5308  
[PERSON], [PERSON], [PERSON], T.  
tragic consequences [7]. Contemporary surveying technologies, such as terrestrial laser scanning and digital photogrammetry, are faster, more reliable, objective, and accurate, so their use is fully justified. For high-rise buildings survey, unmanned aerial vehicles (UAVs or drones) are recommended, as their use overcomes spatial, temporal, and safety limitations typically related to the traditional inspection of these buildings [2; 8]. The application of precise image acquisition techniques represents a fundamental aspect of further image processing [9]. The use of drones for obtaining geoinformation in the form of photographs and spatial data for planning and documenting reconstruction works can be an efficient way of working almost in real time. Even the simplest models of UAVs are equipped with good-quality sensors [10] which enable the collecting of high-resolution images of facade elements. The collected images can be further processed in photogrammetric software in order to generate one large orthomosaic (true orthophoto) image of the entire facade--an orthofacade. The resulting orthofacade allows detailed visual inspection of the facade by analysing a single image, as well as damage mapping. In the case of high-rise buildings, a special difficulty with traditional (eye) image inspection is the size of the orthofacade image, as it requires high-performance computer resources and a lot of experts' time to review. Moreover, especially vague and small cracks are difficult to recognize. Automating image-based crack detection would improve the process of visual inspection, especially in terms of efficiency and accuracy. Automation would save time and eliminate errors in the subjective assessment. In the past decade, techniques for automatic crack identification based on image analysis have been the subject of extensive research [11]. The techniques can be classified into two categories: (1) image processing and (2) machine learning. Both approaches have their advantages and limitations and more research is needed in order to improve their performance and accuracy level. Most of the used techniques address crack detection, without considering the geometric parameters of detected cracks. In addition, there is a limited number of studies related to the automatic detection of facade cracks based on UAV images, whereby the detection is mainly based on individual images [3; 12; 13; 14] or in some cases on one large, stitched facade image [15; 16]. Image stitching is generally recommended only for small datasets, while for large datasets, orthomosaic generation should be used [17]. To obtain cracks' location information, previous research [16] used data from UAV sensors. However, drones with a standard global navigation satellite systems (GNSS) receiver provide image geolocation with an expected accuracy of a few meters [18] and in urban areas, the signal is often interrupted by the roof and/or wall of the buildings [8]. The accuracy in the identification of the crack location can be increased (from meters to centimetres) by the use of ground control points (GCPs) through the georeferencing process of collected UAV images. The paper proposes an _Assisted Inspection Method_ (AIM) developed for the purpose of crack inspection of a building facade, based on the automated analysis of the georeferenced orthofacade. To the best knowledge of the authors, in the domain of building facade, crack inspection from a UAV-based georeferenced orthofacade has not been presented before. In the domain of concrete bridge inspection, several studies have focused on crack analysis from UAV-based images [19; 20] or video [21].