HazardNet: A Small-Scale Vision Language Model for Real-Time Traffic Safety Detection at Edge Devices

• URL: http://arxiv.org/abs/2502.20572v1
• Date: Thu, 27 Feb 2025 22:21:45 GMT
• Title: HazardNet: A Small-Scale Vision Language Model for Real-Time Traffic Safety Detection at Edge Devices
• Authors: Mohammad Abu Tami, Mohammed Elhenawy, Huthaifa I. Ashqar,
• Abstract summary: This paper introduces HazardNet, a small-scale Vision Language Model designed to enhance traffic safety.<n>We built HazardNet by fine-tuning the pre-trained Qwen2-VL-2B model, chosen for its superior performance among open-source alternatives.<n>We present HazardQA, a novel Vision Question Answering dataset constructed specifically for training HazardNet on real-world scenarios.
• Score: 5.233512464561313
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traffic safety remains a vital concern in contemporary urban settings, intensified by the increase of vehicles and the complicated nature of road networks. Traditional safety-critical event detection systems predominantly rely on sensor-based approaches and conventional machine learning algorithms, necessitating extensive data collection and complex training processes to adhere to traffic safety regulations. This paper introduces HazardNet, a small-scale Vision Language Model designed to enhance traffic safety by leveraging the reasoning capabilities of advanced language and vision models. We built HazardNet by fine-tuning the pre-trained Qwen2-VL-2B model, chosen for its superior performance among open-source alternatives and its compact size of two billion parameters. This helps to facilitate deployment on edge devices with efficient inference throughput. In addition, we present HazardQA, a novel Vision Question Answering (VQA) dataset constructed specifically for training HazardNet on real-world scenarios involving safety-critical events. Our experimental results show that the fine-tuned HazardNet outperformed the base model up to an 89% improvement in F1-Score and has comparable results with improvement in some cases reach up to 6% when compared to larger models, such as GPT-4o. These advancements underscore the potential of HazardNet in providing real-time, reliable traffic safety event detection, thereby contributing to reduced accidents and improved traffic management in urban environments. Both HazardNet model and the HazardQA dataset are available at https://huggingface.co/Tami3/HazardNet and https://huggingface.co/datasets/Tami3/HazardQA, respectively.

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