# Research on a Unified Multi-Type Defect Detection Method for Lithium Batteries Throughout Their Entire Lifecycle Based on Multimodal Fusion and Attention-Enhanced YOLOv8

## TL;DR

This paper introduces a new YOLOv8-based model for detecting lithium battery defects using multimodal fusion and attention mechanisms, achieving high accuracy and efficiency across the battery lifecycle.

## Contribution

A novel attention-enhanced YOLOv8 model with multimodal fusion for unified, full-lifecycle defect detection in lithium batteries.

## Key findings

- The model achieves an mAP@0.5 of 87.5% and a minute defect recall rate of 84.1%.
- It processes images at 35.9 FPS on servers and 25.7 FPS on edge devices with low latency.
- The model outperforms YOLOv5/7/8/9-S in key metrics and maintains stable performance across battery lifecycles.

## Abstract

To address the limitations of traditional lithium battery defect detection—low efficiency, high missed detection rates for minute/composite defects, and inadequate multimodal fusion—this study develops an improved YOLOv8 model based on multimodal fusion and attention enhancement for unified full-lifecycle multi-type defect detection. Integrating visible-light and X-ray modalities, the model incorporates a Squeeze-and-Excitation (SE) module to dynamically weight channel features, suppressing redundancy and highlighting cross-modal complementarity. A Multi-Scale Fusion Module (MFM) is constructed to amplify subtle defect expression by fusing multi-scale features, building on established feature fusion principles. Experimental results show that the model achieves an mAP@0.5 of 87.5%, a minute defect recall rate (MRR) of 84.1%, and overall industrial recognition accuracy of 97.49%. It operates at 35.9 FPS (server) and 25.7 FPS (edge) with end-to-end latency of 30.9–38.9 ms, meeting high-speed production line requirements. Exhibiting strong robustness, the lightweight model outperforms YOLOv5/7/8/9-S in core metrics. Large-scale verification confirms stable performance across the battery lifecycle, providing a reliable solution for industrial defect detection and reducing production costs.