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Challenges of Crop Classification from Satellite Imagery with Eurocrops Dataset
(Wydawnictwo Politechniki Łódzkiej, 2023) Aszkowski, Przemysław; Kraft, Marek
Crops monitoring and classification on a nationwide level provide important information for sustainable agricultural management, food security, and policy-making. Recent technological advancements, followed by Earth observation programmes like Copernicus, have provided plenty of publicly available multispectral data. Combining these data with field annotations allows for continuous crop monitoring from publicly available data. In this paper, we present a solution for crop classification to determine crop type from Sentinel-2 multispectral data, utilizing machine learning techniques. Apart from presenting initial results, we discuss the challenges of crop classification on a Eurocrops dataset and further research directions.
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.
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.