01501nas a2200169   4500000000100000008004100001260001200042100002000054700002000074700002000094700002200114700002600136245009200162856007700254490001200331520098800343       2023                            d  c10/20231 aBartosz Wójcik1 aMateusz Żarski1 aKamil Książek1 aJaroslaw Miszczak1 aMirosław Skibniewski00aA deep learning method for hard-hat-wearing detection based on head center localization  uhttps://journals.pan.pl/dlibra/publication/147340/edition/128931/content0 v71 (EA)3 aIn recent years, a lot of attention is 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 rule-based 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.01905nas a2200169   4500000000100000008004100001260001200042100002200054700002600076700002000102700002000122700002000142700002200162245011400184856006400298520137300362       2023                            d  c01/20231 aMariusz Ostrowski1 aBartlomiej Blachowski1 aBartosz Wójcik1 aMateusz Żarski1 aPiotr Tauzowski1 aŁukasz Jankowski00aA framework for computer vision-based health monitoring of a truss structure subjected to unknown excitations  uhttps://link.springer.com/article/10.1007/s11803-023-2154-33 aComputer vision (CV) methods for measurement of structural vibration are less expensive, and their application is more straightforward than methods based on sensors that measure physical quantities at particular points of a structure. However, CV methods produce significantly more measurement errors. Thus, computer vision-based structural health monitoring (CVSHM) requires appropriate methods of damage assessment that are robust with respect to highly contaminated measurement data. In this paper a complete CVSHM framework is proposed, and three damage assessment methods are tested. The first is the augmented inverse estimate (AIE), proposed by Peng et al. in 2021. This method is designed to work with highly contaminated measurement data, but it fails with a large noise provided by CV measurement. The second method, as proposed in this paper, is based on the AIE, but it introduces a weighting matrix that enhances the conditioning of the problem. The third method, also proposed in this paper, introduces additional constraints in the optimization process; these constraints ensure that the stiff ness of structural elements can only decrease. Both proposed methods perform better than the original AIE. The latter of the two proposed methods gives the best results, and it is robust with respect to the selected coefficients, as required by the algorithm.01237nas a2200169   4500000000100000008004100001260002700042100002600069700002200095700002000117700002000137700002000157700002100177245011400198520073300312020002201045       2022                            d  c06/2022bSpringer Link1 aBartlomiej Blachowski1 aMariusz Ostrowski1 aMateusz Żarski1 aBartosz Wójcik1 aPiotr Tauzowski1 aLukasz Jankowski00aAn Efficient Computer Vision-Based Method for Estimation of Dynamic Displacements in Spatial Truss Structures3 aIn the present study a comparison of frequently used computer vision (CV)-based methods for structural health monitoring of truss structures is shown. The attention is paid to template matching methods that can be classified into one of two groups: area-based and feature-based methods. Synthetic but realistic video is used in this study. Results of the comparison are reliable due to the fact that the exact displacements are known from the finite element model of the investigated structure. From the variety of tested CV methods, the Kanade–Lucas–Tomasi algorithm with FREAK-based repetitive correction outperforms the remaining tested methods in terms of the computation time with a negligibly greater estimation error.  a978-3-031-07257-401380nas a2200169   4500000000100000008004100001260001200042100002000054700002000074700002200094700002600116700002200142245008300164856003700247490000700284520091900291       2022                            d  c10/20221 aMateusz Żarski1 aBartosz Wójcik1 aJaroslaw Miszczak1 aBartlomiej Blachowski1 aMariusz Ostrowski00aComputer Vision Based Inspection on Post-Earthquake With UAV Synthetic Dataset  uhttps://arxiv.org/abs/2210.052820 v103 aThe 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.00538nas a2200157   4500000000100000008004100001260009400042100002000136700002000156700002000176700001800196700002200214245006500236856005700301020002200358       2021                            d  bInstitute of Fundamental Technological Research of the Polish Academy of SciencesaWarsaw1 aMateusz Żarski1 aBartosz Wójcik1 aKamil Książek1 aMarek Salamak1 aJaroslaw Miszczak00aAdvances in applicable deep-learning based defect  detection  uhttp://v4shm.ippt.pan.pl/WEO2021-BookOfAbstracts.pdf  a987-83-65550-23-100496nas a2200145   4500000000100000008004100001260009400042100002000136700002000156700001800176700002200194245005500216856005700271020002200328       2021                            d  bInstitute of Fundamental Technological Research of the Polish Academy of SciencesaWarsaw1 aBartosz Wójcik1 aMateusz Żarski1 aMarek Salamak1 aJaroslaw Miszczak00aExtracting crack characteristics from RGB-D images  uhttp://v4shm.ippt.pan.pl/WEO2021-BookOfAbstracts.pdf  a987-83-65550-23-101578nas a2200145   4500000000100000008004100001260001200042100002000054700002000074700002000094700002200114245012400136490000700260520116500267       2021                            d  c03/20221 aMateusz Żarski1 aBartosz Wójcik1 aKamil Książek1 aJaroslaw Miszczak00aFinicky transfer learning—A method of pruning convolutional neural networks for cracks classification on edge devices0 v373 aHigh demand for computational power significantly limits the possibility of using modern deep learning methods in the environments where one has to deal with devices limited by the performance and the energy constraints. To address this issue, this paper proposes a novel method of combining the pruning and the transfer learning techniques for the purpose of delivering solid accuracy while simultaneously lowering the demand for energy and computing power. This method is referred to as \emph{Finicky Transfer Learning} (FTL) as it is finicky during the process of selecting filters from a pre-trained feature extractor to compose a sparser architecture. The proposed filter selection process is based on an original approach utilizing the Jaccard similarity coefficient calculated between the activation maps and the masks obtained by semantic segmentation. This enables the use of CNN, trained previously on a large generic dataset, in a crack classification task. The presented method significantly lowers the inference time while maintaining or even slightly increasing the classification accuracy, enabling real-time operation on single-board computers.01411nas a2200145   4500000000100000008004100001260001200042100002000054700002000074700002200094245009100116856003700207490000700244520101400251       2021                            d  c12/20211 aMateusz Żarski1 aBartosz Wójcik1 aJaroslaw Miszczak00aKrakN: Transfer Learning framework and dataset for infrastructure thin crack detection  uhttps://arxiv.org/abs/2004.123370 v163 aMonitoring the technical condition of infrastructure is a crucial element of its maintenance. Although there are many deep learning models intended for this purpose, they are severely limited in their application due to labour-intensive gathering of new datasets and high demand for computing power during model training. To overcome these limiting factors we propose a KrakN framework. It enables end-to-end development of unique infrastructure defect detectors on digital images, achieving an accuracy of above 90%. The framework also supports the semi-automatic creation of new datasets and has modest computing power requirements. It can be used to immediately implement deep learning in the process of infrastructure management and, due to its architecture based on transfer learning, allows low metric loss when used across different datasets. We also demonstrate that thanks to its scalability and modular structure, the presented framework is easily modifiable, and can be used in realistic scenarios.00337nas a2200121   4500000000100000008004100001260001100042100002000053700002000073245010000093490000700193022001500200       2021                            d  c6/20211 aBartosz Wójcik1 aMateusz Żarski00aThe measurements of surface defect area with an RGB-D camera for a BIM-backed bridge inspection0 v69  a0239-7528