We conduct a comprehensive study on a new task named power battery detection
(PBD), which aims to localize the dense cathode and anode plates endpoints from
X-ray images to evaluate the quality of power batteries. Existing manufacturers
usually rely on human eye observation to complete PBD, which makes it difficult
to balance the accuracy and efficiency of detection. To address this issue and
drive more attention into this meaningful task, we first elaborately collect a
dataset, called X-ray PBD, which has $1,500$ diverse X-ray images selected from
thousands of power batteries of $5$ manufacturers, with $7$ different visual
interference. Then, we propose a novel segmentation-based solution for PBD,
termed multi-dimensional collaborative network (MDCNet). With the help of line
and counting predictors, the representation of the point segmentation branch
can be improved at both semantic and detail aspects. Besides, we design an
effective distance-adaptive mask generation strategy, which can alleviate the
visual challenge caused by the inconsistent distribution density of plates to
provide MDCNet with stable supervision. Without any bells and whistles, our
segmentation-based MDCNet consistently outperforms various other corner
detection, crowd counting and general/tiny object detection-based solutions,
making it a strong baseline that can help facilitate future research in PBD.
Finally, we share some potential difficulties and works for future researches.
The source code and datasets will be publicly available at
\href{this http URL}{X-ray
PBD}.

我们对一项名为电池检测（PBD）的新任务进行了全面研究，旨在从 X 射线图像中定位密集的阴极和阳极板端点，以评估电池的质量。通过精心收集 X 射线 PBD 数据集，我们提出了一种基于分割的新解决方案 MDCNet，它能够在语义和细节方面改善点分割分支的表示。与其他基于角点检测、人群计数和一般 / 小物体检测的解决方案相比，我们的分割式 MDCNet 持续表现出色，成为未来 PBD 研究的强有力基准。