用视觉问答模型零样本检测电池热成像异常，无需训练数据。
Zero-Shot Anomaly Detection in Battery Thermal Images Using Visual Question Answering with Prior Knowledge
- 用预训练模型+文本提示，利用正常电池行为知识实现零样本检测。
- 在无电池数据训练下，性能媲美需大量标注数据的现有方法。
- 适合缺乏标注数据、追求快速部署的电池安全监测场景。
电池广泛应用于电动汽车和可再生能源储能，其安全与效率至关重要。热成像异常检测有助于早期发现故障，但传统深度学习方法依赖大量标注数据，而异常数据因安全风险和采集成本高难以获取。为此，我们探索基于视觉问答（VQA）的零样本异常检测，利用预训练模型和文本提示，结合正常电池热行为先验知识，实现无需电池特定训练数据的异常识别。我们评估了三种VQA模型（ChatGPT-4o、LLaVa-13b、BLIP-2），分析其对提示变化、重复试验及输出质量的鲁棒性。尽管未在电池数据上微调，该方法性能仍可与使用电池数据训练的先进模型相媲美。研究结果表明，基于VQA的零样本学习在电池异常检测中具有潜力，并指明了未来改进方向。
原文摘要 · Abstract (English)
Batteries are essential for various applications, including electric vehicles and renewable energy storage, making safety and efficiency critical concerns. Anomaly detection in battery thermal images helps identify failures early, but traditional deep learning methods require extensive labeled data, which is difficult to obtain, especially for anomalies due to safety risks and high data collection costs. To overcome this, we explore zero-shot anomaly detection using Visual Question Answering (VQA) models, which leverage pretrained knowledge and textbased prompts to generalize across vision tasks. By incorporating prior knowledge of normal battery thermal behavior, we design prompts to detect anomalies without battery-specific training data. We evaluate three VQA models (ChatGPT-4o, LLaVa-13b, and BLIP-2) analyzing their robustness to prompt variations, repeated trials, and qualitative outputs. Despite the lack of finetuning on battery data, our approach demonstrates competitive performance compared to state-of-the-art models that are trained with the battery data. Our findings highlight the potential of VQA-based zero-shot learning for battery anomaly detection and suggest future directions for improving its effectiveness.
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