<?xml version="1.0" encoding="UTF-8"?>
<xml><records><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Bartosz Wójcik</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mateusz Żarski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Kamil Książek</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jaroslaw Miszczak</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mirosław Skibniewski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">A deep learning method for hard-hat-wearing detection based on head center localization</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2023</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Bulletin of the Polish Academy of Sciences Technical Sciences</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">71 (EA)</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">6</style></ISSUE><DATE><style face="normal" font="default" size="100%">10/2023</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">In 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.</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://journals.pan.pl/dlibra/publication/147340/edition/128931/content</style></URL><title><style face="normal" font="default" size="100%">A deep learning method for hard-hat-wearing detection based on head center localization</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mariusz Ostrowski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Bartlomiej Blachowski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Bartosz Wójcik</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mateusz Żarski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Tauzowski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Łukasz Jankowski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">A framework for computer vision-based health monitoring of a truss structure subjected to unknown excitations</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2023</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Earthquake Engineering and Engineering Vibration</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><DATE><style face="normal" font="default" size="100%">01/2023</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">Computer 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.</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://link.springer.com/article/10.1007/s11803-023-2154-3</style></URL><title><style face="normal" font="default" size="100%">A framework for computer vision-based health monitoring of a truss structure subjected to unknown excitations</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Bartlomiej Blachowski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mariusz Ostrowski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mateusz Żarski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Bartosz Wójcik</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Piotr Tauzowski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Lukasz Jankowski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">An Efficient Computer Vision-Based Method for Estimation of Dynamic Displacements in Spatial Truss Structures</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Workshop on Structural Health Monitoring</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Springer Link</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">06/2022</style></DATE><ISBN><style face="normal" font="default" size="100%">978-3-031-07257-4</style></ISBN><ABSTRACT><style face="normal" font="default" size="100%">In 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.</style></ABSTRACT><title><style face="normal" font="default" size="100%">An Efficient Computer Vision-Based Method for Estimation of Dynamic Displacements in Spatial Truss Structures</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mateusz Żarski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Bartosz Wójcik</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jaroslaw Miszczak</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Bartlomiej Blachowski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mariusz Ostrowski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Computer Vision Based Inspection on Post-Earthquake With UAV Synthetic Dataset</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2022</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE Access</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">10</style></VOLUME><SECTION><style face="normal" font="default" size="100%">108134</style></SECTION><DATE><style face="normal" font="default" size="100%">10/2022</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">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.</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://arxiv.org/abs/2210.05282</style></URL><title><style face="normal" font="default" size="100%">Computer Vision Based Inspection on Post-Earthquake With UAV Synthetic Dataset</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>7</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mateusz Żarski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Bartosz Wójcik</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Kamil Książek</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Marek Salamak</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jaroslaw Miszczak</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Advances in applicable deep-learning based defect  detection</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">2nd Workshop on Engineering  Optimization – WEO 2021</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Warsaw</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Institute of Fundamental Technological Research of the Polish Academy of Sciences</style></PUBLISHER><ISBN><style face="normal" font="default" size="100%">987-83-65550-23-1</style></ISBN><URL><style face="normal" font="default" size="100%">http://v4shm.ippt.pan.pl/WEO2021-BookOfAbstracts.pdf</style></URL><title><style face="normal" font="default" size="100%">Advances in applicable deep-learning based defect  detection</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>7</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Bartosz Wójcik</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mateusz Żarski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Marek Salamak</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jaroslaw Miszczak</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Extracting crack characteristics from RGB-D images</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">2nd Workshop on Engineering  Optimization – WEO 2021</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Warsaw</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">Institute of Fundamental Technological Research of the Polish Academy of Sciences</style></PUBLISHER><ISBN><style face="normal" font="default" size="100%">987-83-65550-23-1</style></ISBN><URL><style face="normal" font="default" size="100%">http://v4shm.ippt.pan.pl/WEO2021-BookOfAbstracts.pdf</style></URL><title><style face="normal" font="default" size="100%">Extracting crack characteristics from RGB-D images</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mateusz Żarski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Bartosz Wójcik</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Kamil Książek</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jaroslaw Miszczak</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Finicky transfer learning—A method of pruning convolutional neural networks for cracks classification on edge devices</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Computer-Aided Civil And Infrastructure Engineering</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">37</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">4</style></ISSUE><SECTION><style face="normal" font="default" size="100%">500</style></SECTION><DATE><style face="normal" font="default" size="100%">03/2022</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">High 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.</style></ABSTRACT><title><style face="normal" font="default" size="100%">Finicky transfer learning—A method of pruning convolutional neural networks for cracks classification on edge devices</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mateusz Żarski</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Bartosz Wójcik</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Jaroslaw Miszczak</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">KrakN: Transfer Learning framework and dataset for infrastructure thin crack detection</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">SoftwareX</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">16</style></VOLUME><SECTION><style face="normal" font="default" size="100%">100893</style></SECTION><DATE><style face="normal" font="default" size="100%">12/2021</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">Monitoring 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.</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://arxiv.org/abs/2004.12337</style></URL><title><style face="normal" font="default" size="100%">KrakN: Transfer Learning framework and dataset for infrastructure thin crack detection</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Bartosz Wójcik</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mateusz Żarski</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">The measurements of surface defect area with an RGB-D camera for a BIM-backed bridge inspection</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Bulletin of the Polish Academy of Sciences: Technical Sciences</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">69</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">3</style></ISSUE><DATE><style face="normal" font="default" size="100%">6/2021</style></DATE><title><style face="normal" font="default" size="100%">The measurements of surface defect area with an RGB-D camera for a BIM-backed bridge inspection</style></title></RECORD></records></xml>
