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}.

翻译：动力电池是电动汽车的关键组件，其内部结构缺陷可能引发严重的安全隐患。本文系统研究了一项新任务——动力电池检测（PBD），旨在从工业X射线图像中定位正负极极片密集端点以实现质量检测。人工检测效率低且易出错，传统视觉算法难以应对极片密集排列、低对比度、尺度变化及成像伪影等挑战。为解决这一问题并推动该重要任务的研究，我们构建了首个大规模基准数据集PBD5K，包含来自9种电池类型的5000张X射线图像，提供精细标注和8类真实视觉干扰。为实现可扩展且一致的标注，我们开发了智能标注管线，融合图像过滤、模型辅助预标注、交叉验证及分层质量评估。我们将PBD定义为点级分割问题，并提出MDCNeXt模型，该模型通过提取并融合极片自身的点、线和计数等多维结构线索进行检测。为提升极片判别能力并抑制视觉干扰，MDCNeXt包含两个状态空间模块：一是提示过滤模块，通过任务特定提示学习对比关系；二是密度感知重排序模块，用于优化高密度极片区域的分割结果。此外，我们提出距离自适应掩码生成策略，以在正负极位置空间分布变化时提供稳健监督。源代码与数据集将在\href{https://github.com/Xiaoqi-Zhao-DLUT/X-ray-PBD}{PBD5K}公开。