  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{https://github.com/Xiaoqi-Zhao-DLUT/X-ray-PBD}{X-ray PBD}.
