Abstract:Digitalization of existing buildings and the creation of 3D BIM models for them has become crucial for many tasks. Of particular importance are floor plans, which contain information about building layouts and are vital for processes such as construction, maintenance or refurbishing. However, this data is not always available in digital form, especially for older buildings constructed before CAD tools were widely available, or lacks semantic information. The digitalization of such information usually requires manual work of an expert that must reconstruct the layouts by hand, which is a cumbersome and error-prone process. In this paper, we present a pipeline for reconstruction of vectorized 3D models from scanned 2D plans, aiming at increasing the efficiency of this process. The method presented achieves state-of-the-art results in the public dataset CubiCasa5k, and shows good generalization to different types of plans. Our vectorization approach is particularly effective, outperforming previous methods.
Abstract:Infrastructure inspection is a very costly task, requiring technicians to access remote or hard-to-reach places. This is the case for power transmission towers, which are sparsely located and require trained workers to climb them to search for damages. Recently, the use of drones or helicopters for remote recording is increasing in the industry, sparing the technicians this perilous task. This, however, leaves the problem of analyzing big amounts of images, which has great potential for automation. This is a challenging task for several reasons. First, the lack of freely available training data and the difficulty to collect it complicate this problem. Additionally, the boundaries of what constitutes a damage are fuzzy, introducing a degree of subjectivity in the labelling of the data. The unbalanced class distribution in the images also plays a role in increasing the difficulty of the task. This paper tackles the problem of structural damage detection in transmission towers, addressing these issues. Our main contributions are the development of a system for damage detection on remotely acquired drone images, applying techniques to overcome the issue of data scarcity and ambiguity, as well as the evaluation of the viability of such an approach to solve this particular problem.