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

TL;DR

This paper proposes a zero-shot anomaly detection method for battery thermal images using Visual Question Answering models, leveraging prior knowledge and prompts to identify failures without needing labeled anomaly data.

Contribution

It introduces a novel approach combining VQA models with prior knowledge for zero-shot battery anomaly detection, avoiding the need for extensive labeled datasets.

Findings

VQA models can detect anomalies without battery-specific training.

The approach is robust to prompt variations and repeated trials.

Competitive performance achieved compared to supervised models.

Abstract

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.