Vehicle Detection Performance in Nordic Region ...

• Main Authors: Mokayed, Hamam, Saini, Rajkumar, Adewumi, Oluwatosin, Alkhaled, Lama, Backe, Bjorn, Shivakumara, Palaiahnakote, Hagner, Olle, Hum, Yan Chai
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2024
• Subjects: Computer Vision and Pattern Recognition cs.CV; Machine Learning cs.LG; FOS Computer and information sciences; Northern Sweden
• Online Access: https://dx.doi.org/10.48550/arxiv.2403.15017; https://arxiv.org/abs/2403.15017

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 has UAV 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, 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 novel strategies designed ... : submitted to ICPR2024 ...