Towards Automatic Power Battery Detection: New Challenge Benchmark Dataset and Baseline
Authors: Xiaoqi Zhao, Youwei Pang, Zhenyu Chen, Qian Yu, Lihe Zhang, Hanqi Liu, Jiaming Zuo, Huchuan Lu

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 1500 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 .