Relation-aware graph convolutional network for waste battery inspection based on X-ray images

Abstract

With the popularization of various electronic devices, the demand for batteries is rapidly increasing. Batteries bring convenience to our daily lives, but how to effectively achieve battery recycling is a challenge. In fact, improper disposal can cause serious environmental pollution and resource waste. Currently, it is inefficient and time-consuming to manually locate batteries from waste electronic devices by employing a large number of professional workers. Therefore, we propose an efficient solution based on deep learning and X-ray images, which can automatically detect batteries without opening waste electronic devices. Specifically, we construct a High-quality X-ray Battery Inspection dataset called HiXray-BI, which not only contains 6000 X-ray images collected from the existing dataset, but also provides 23,994 battery instances. In addition, we propose a novel deep learning model called Relation-Aware Graph Convolutional Network (RA-GCN) for X-ray waste battery inspection, which introduces a novel Multi-scale Feature Relation Module (MFRM) and Semantic Feature Relation Module (SFRM) to improve detection performance. Experimental results on the HiXray-BI dataset show that our method compares favorably with state-of-the-art methods. Our solution provides new insights into recycling waste batteries, which is beneficial for environmental protection and sustainable development.