BullySense: A Approach for Campus Bullying Detection Leveraging LLava Foundation Model

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 precision of 0.982 and F1 scores of 0.977. 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.