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Stały URI dla kolekcjihttp://hdl.handle.net/11652/4775

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20232

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Autorsearch.filters.applied.operator.equals: search.filters.author.Kraft, MarekTematsearch.filters.applied.operator.equals: search.filters.subject.deep learning

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Teraz wyświetlane 1 - 2 z 2
  • Pozycja
    Spotting Advertisements from Above: Billboard Detection and Segmentation in UAV Imagery
    (Wydawnictwo Politechniki Łódzkiej, 2023) Ptak, Bartosz; Dominiak, Jan; Kraft, Marek
    In this work, deep-learning methods were researched for billboard detection in urban environments. Billboards are one of the adversarial visual pollutants occurring in cities, causing over-saturation of visual stimulation. Due to this, we develop an algorithm that helps in the analysis and management of urban space. We utilise near real-time object detection methods to detect and segment them on images registered by unmanned aerial vehicles (UAVs). Research is based on recent algorithms from the YOLO family with modified heads for the instance segmentation task. We gathered images and prepared hand-annotated labels for training and evaluation purposes of deep learning approaches. We reached the mAP@0.5 metric of 0.61 for detection and 0.60 for segmentation, enabling us to develop smart city applications.
  • Pozycja
    Fault Diagnosis in a Squirrel-Cage Induction Motor Using Thermal Imaging
    (Wydawnictwo Politechniki Łódzkiej, 2023) Piechocki, Mateusz; Kraft, Marek; Pajchrowski, Tomasz
    Fault diagnosis is a vivid topic in industrial applications or intelligent building solutions. One of the well-established techniques involves the measurement and analysis of current signals. However, this method has several significant drawbacks, such as the inability to inspect during machinery operation or the lack of precise information on the malfunction location. This article proposes a non-invasive method for squirrel-cage induction motor’s state classification and fault diagnosis. The approach is based on thermal image analysis that utilizes a compact convolution neural network. In addition, the gathered and annotated image set, which consists of thermal images with 640 x 512 pixels resolution, is presented.