VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual
  Navigation
Hao Wang Jiayou Qin Ashish Bastola Xiwen Chen John Suchanek Zihao Gong Abolfazl Razi
This paper explores the potential of Large Language Models(LLMs) in zero-shot
anomaly detection for safe visual navigation. With the assistance of the
state-of-the-art real-time open-world object detection model Yolo-World and
specialized prompts, the proposed framework can identify anomalies within
camera-captured frames that include any possible obstacles, then generate
concise, audio-delivered descriptions emphasizing abnormalities, assist in safe
visual navigation in complex circumstances. Moreover, our proposed framework
leverages the advantages of LLMs and the open-vocabulary object detection model
to achieve the dynamic scenario switch, which allows users to transition
smoothly from scene to scene, which addresses the limitation of traditional
visual navigation. Furthermore, this paper explored the performance
contribution of different prompt components, provided the vision for future
improvement in visual accessibility, and paved the way for LLMs in video
anomaly detection and vision-language understanding.