Computer Vision based inspection on post-earthquake with UAV synthetic dataset

The area affected by the earthquake is vast and often difficult to entirely cover, and the earthquake itself is a sudden event that causes multiple defects simultaneously, that cannot be effectively traced using traditional, manual methods. This article presents an innovative approach to the problem of detecting damage after sudden events by using an interconnected set of deep machine learning models organized in a single pipeline and allowing for easy modification and swapping models seamlessly. Models in the pipeline were trained with a synthetic dataset and were adapted to be further evaluated and used with unmanned aerial vehicles (UAVs) in real-world conditions. Thanks to the methods presented in the article, it is possible to obtain high accuracy in detecting buildings defects, segmenting constructions into their components and estimating their technical condition based on a single drone flight.Comment: 15 pages, 8 figures, published version, software available from https://github.com/MatZar01/IC_SHM_P

The Effectiveness of World Models for Continual Reinforcement Learning

World models power some of the most efficient reinforcement learning algorithms. In this work, we showcase that they can be harnessed for continual learning - a situation when the agent faces changing environments. World models typically employ a replay buffer for training, which can be naturally extended to continual learning. We systematically study how different selective experience replay methods affect performance, forgetting, and transfer. We also provide recommendations regarding various modeling options for using world models. The best set of choices is called Continual-Dreamer, it is task-agnostic and utilizes the world model for continual exploration. Continual-Dreamer is sample efficient and outperforms state-of-the-art task-agnostic continual reinforcement learning methods on Minigrid and Minihack benchmarks.Comment: Accepted at CoLLAs 2023, 21 pages, 15 figure

The measurements of surface defect area with an RGB-D camera for a BIM-backed bridge inspection

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