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Vehicle Detection Performance in Nordic Region

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Pattern Recognition (ICPR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15322))

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Abstract

This paper addresses the critical challenge of vehicle detection in the harsh winter conditions in the Nordic regions, characterized by heavy snowfall, reduced visibility, and low lighting. Due to their susceptibility to environmental distortions and occlusions, traditional vehicle detection methods have struggled in these adverse conditions. The advanced proposed deep learning architectures brought promise, yet the unique difficulties of detecting vehicles in Nordic winters remain inadequately addressed. This study uses the Nordic Vehicle Dataset (NVD), which contains UAV (unmanned aerial vehicle) images from northern Sweden, to evaluate the performance of state-of-the-art vehicle detection algorithms under challenging weather conditions. Our methodology includes a comprehensive evaluation of single-stage, two-stage, segmentation-based, and transformer-based detectors against the NVD. We propose a series of enhancements tailored to each detection framework, including data augmentation, hyperparameter tuning, transfer learning, and Specifically implementing and enhancing the Detection Transformer (DETR). A novel architecture is proposed that leverages self-attention mechanisms with the help of MSER (maximally stable extremal regions) and RST (Rough Set Theory) to identify and filter the region that model long-range dependencies and complex scene contexts. Our findings not only highlight the limitations of current detection systems in the Nordic environment but also offer promising directions for enhancing these algorithms for improved robustness and accuracy in vehicle detection amidst the complexities of winter landscapes. The code and the dataset are available at https://nvd.ltu-ai.dev.

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Author information

Authors and Affiliations

  1. Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden

    Hamam Mokayed, Rajkumar Saini, Oluwatosin Adewumi, Lama Alkhaled & Björn Backe

  2. School of Science, Engineering and Environment, Salford University, Salford, UK

    Palaiahnakote Shivakumara

  3. Smartplanes, Jävre, Sweden

    Olle Hagner

  4. Mechatronics and Biomedical Engineering, Universiti Tunku Abdul Rahman, Petaling Jaya, Selangor, Malaysia

    Yan Chai Hum

Authors
  1. Hamam Mokayed
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  2. Rajkumar Saini
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  3. Oluwatosin Adewumi
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  4. Lama Alkhaled
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  5. Björn Backe
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  6. Palaiahnakote Shivakumara
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  7. Olle Hagner
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  8. Yan Chai Hum
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Corresponding author

Correspondence to Hamam Mokayed .

Editor information

Editors and Affiliations

  1. University of Salford, Salford, Lancashire, UK

    Apostolos Antonacopoulos

  2. Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    Subhasis Chaudhuri

  3. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

  4. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  5. IIT Kharagpur, Kharagpur, West Bengal, India

    Saumik Bhattacharya

  6. Indian Statistical Institute Kolkata, Kolkata, West Bengal, India

    Umapada Pal

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Mokayed, H. et al. (2025). Vehicle Detection Performance in Nordic Region. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15322. Springer, Cham. https://doi.org/10.1007/978-3-031-78312-8_5

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  • DOI: https://doi.org/10.1007/978-3-031-78312-8_5

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