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<p>Campus bullying has become a significant social issue, with profound impacts on the mental and physical well-being of students. Timely and effective detection is crucial for addressing this problem. To tackle these challenges, this study introduces BullySense, a campus bullying detection model built on the vision language model (VLM) framework. Utilizing the pre-trained LLaVA-1.5 model, BullySense is fine-tuned with a curated dataset of bullying and normal campus activity images, employing Low-Rank Adaptation (LoRA) to enhance task-specific performance. Experimental results show that BullySense outperforms traditional models, achieving a precision of 0.982 and F1 scores of 0.977. In practical deployment on edge devices, the system attained a detection latency of only 0.83&#xa0;s. While challenges remain, such as handling low-quality images and extending to multimodal data, this work demonstrates the potential of AI for improving campus safety and lays the groundwork for future advancements.</p>

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BullySense: an approach for campus bullying detection leveraging LLaVA foundation model

  • 期刊论文

  • 发表日期：

    2026年3月25日

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  • Houming Gong,
  • Tao Li,
  • Cong Chen,
  • Wei Lu,
  • Wei Qu,
  • Ruiqi Du

摘要

Campus bullying has become a significant social issue, with profound impacts on the mental and physical well-being of students. Timely and effective detection is crucial for addressing this problem. To tackle these challenges, this study introduces BullySense, a campus bullying detection model built on the vision language model (VLM) framework. Utilizing the pre-trained LLaVA-1.5 model, BullySense is fine-tuned with a curated dataset of bullying and normal campus activity images, employing Low-Rank Adaptation (LoRA) to enhance task-specific performance. Experimental results show that BullySense outperforms traditional models, achieving a precision of 0.982 and F1 scores of 0.977. In practical deployment on edge devices, the system attained a detection latency of only 0.83 s. While challenges remain, such as handling low-quality images and extending to multimodal data, this work demonstrates the potential of AI for improving campus safety and lays the groundwork for future advancements.