Fractional B-Spline Wavelets and U-Net Architecture for Robust and Reliable Vehicle Detection in Snowy Conditions

This paper addresses the critical need for advanced real-time vehicle detection methodologies in Vehicle Intelligence Systems (VIS), especially in the context of using Unmanned Aerial Vehicles (UAVs) for data acquisition in severe weather conditions, such as heavy snowfall typical of the Nordic regi...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 24; no. 12; p. 3938
Main Authors:	Mokayed, Hamam, Ulehla, Christián, Shurdhaj, Elda, Nayebiastaneh, Amirhossein, Alkhaled, Lama, Hagner, Olle, Hum, Yan Chai
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 18-06-2024 MDPI
Subjects:	Accuracy Centre - ProcessIT Innovations Centrumbildning - ProcessIT Innovations Communication Datasets Drones Edge computing fractional B-spline harsh weathers Machine Learning Maskininlärning Performance evaluation U-Net vehicle detection Vehicles Wavelet transforms vehicle detection harsh weathers U-Net fractional B-spline
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Summary:	This paper addresses the critical need for advanced real-time vehicle detection methodologies in Vehicle Intelligence Systems (VIS), especially in the context of using Unmanned Aerial Vehicles (UAVs) for data acquisition in severe weather conditions, such as heavy snowfall typical of the Nordic region. Traditional vehicle detection techniques, which often rely on custom-engineered features and deterministic algorithms, fall short in adapting to diverse environmental challenges, leading to a demand for more precise and sophisticated methods. The limitations of current architectures, particularly when deployed in real-time on edge devices with restricted computational capabilities, are highlighted as significant hurdles in the development of efficient vehicle detection systems. To bridge this gap, our research focuses on the formulation of an innovative approach that combines the fractional B-spline wavelet transform with a tailored U-Net architecture, operational on a Raspberry Pi 4. This method aims to enhance vehicle detection and localization by leveraging the unique attributes of the NVD dataset, which comprises drone-captured imagery under the harsh winter conditions of northern Sweden. The dataset, featuring 8450 annotated frames with 26,313 vehicles, serves as the foundation for evaluating the proposed technique. The comparative analysis of the proposed method against state-of-the-art detectors, such as YOLO and Faster RCNN, in both accuracy and efficiency on constrained devices, emphasizes the capability of our method to balance the trade-off between speed and accuracy, thereby broadening its utility across various domains.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1424-8220 1424-8220
DOI:	10.3390/s24123938