Power Battery Detection

Xiaoqi Zhao; Peiqian Cao; Chenyang Yu; Zonglei Feng; Lihe Zhang; Hanqi Liu; Jiaming Zuo; Youwei Pang; Jinsong Ouyang; Weisi Lin; Georges El Fakhri; Huchuan Lu; Xiaofeng Liu

arXiv · 2508.07797

Power Battery Detection

Xiaoqi Zhao·Peiqian Cao·Chenyang Yu·Zonglei Feng·Lihe Zhang·Hanqi Liu·Jiaming Zuo·Youwei Pang·Jinsong Ouyang·Weisi Lin·Georges El Fakhri·Huchuan Lu·Xiaofeng Liu

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Abstract

Power batteries are essential components in electric vehicles, where internal structural defects can pose serious safety risks. We conduct a comprehensive study on a new task, power battery detection (PBD), which aims to localize the dense endpoints of cathode and anode plates from industrial X-ray images for quality inspection. Manual inspection is inefficient and error-prone, while traditional vision algorithms struggle with densely packed plates, low contrast, scale variation, and imaging artifacts. To address this issue and drive more attention into this meaningful task, we present PBD5K, the first large-scale benchmark for this task, consisting of 5,000 X-ray images from nine battery types with fine-grained annotations and eight types of real-world visual interference. To support scalable and consistent labeling, we develop an intelligent annotation pipeline that combines image filtering, model-assisted pre-labeling, cross-verification, and layered quality evaluation. We formulate PBD as a point-level segmentation problem and propose MDCNeXt, a model designed to extract and integrate multi-dimensional structure clues including point, line, and count information from the plate itself. To improve discrimination between plates and suppress visual interference, MDCNeXt incorporates two state space modules. The first is a prompt-filtered module that learns contrastive relationships guided by task-specific prompts. The second is a density-aware reordering module that refines segmentation in regions with high plate density. In addition, we propose a distance-adaptive mask generation strategy to provide robust supervision under varying spatial distributions of anode and cathode positions. The source code and datasets will be publicly available at \href{https://github.com/Xiaoqi-Zhao-DLUT/X-ray-PBD}{PBD5K}.

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BibTeX RIS

Xiaoqi Zhao, Peiqian Cao, Chenyang Yu, Zonglei Feng, Lihe Zhang, Hanqi Liu, Jiaming Zuo, Youwei Pang, Jinsong Ouyang, Weisi Lin, Georges El Fakhri, Huchuan Lu, Xiaofeng Liu. 2026-06-13. Power Battery Detection. https://arxiv.org/abs/2508.07797

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