Zero-Shot Anomaly Detection in Battery Thermal Images Using Visual Question Answering with Prior Knowledge

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.