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Waste management plays an important role in environmental protection and resource recycling. In fact, traditional waste recycling requires a large number of workers to sort manually, which is not only labor-intensive and time-consuming, but also increases the health risk for workers. To further improve the efficiency of waste detection and classification, related scholars have used computer vision technology to achieve automatic waste management. However, existing methods tend to use RGB images as input, requiring workers to open waste bags and disperse waste items in advance. To avoid comprehensive preparation procedures and identify occluded waste items, we use deep learning models to detect and recognize waste items in X-ray images. Compared to RGB images, X-ray images can effectively preserve boundary information and reflect material types. To take advantage of the inherent characteristics of X-ray images, we propose a novel X-ray Characteristic Enhancement Module (XCEM) to improve the performance of X-ray waste detection. XCEM consists of three sub-modules, namely Hybrid Interaction Module (HIM), Multi-scale Material Perception Module (MMPM), and Boundary Guidance Module (BGM). Specifically, HIM not only generates soft and hard boundary maps, but also achieves information interaction and feature enhancement of different boundary maps. Meanwhile, MMPM can effectively capture different multi-scale material features. In addition, BGM uses auxiliary boundary information and attention mechanisms to effectively enhance critical material features. As a plug-and-play module, XCEM can be readily inserted into the most popular detection pipelines. We select several representative one-stage and two-stage methods and perform related experiments on the WIXRay dataset. Experimental results show that our method can achieve consistent performance gains on diverse detection paradigms. This study promotes the development of intelligent waste inspection and recycling. • An efficient solution based on X-ray images for waste detection and classification. • The proposed XCEM can make use of the inherent characteristics of X-ray images. • The XCEM can achieve consistent performance gains on diverse detection models. • Several representative methods are explored for waste detection in X-ray images. • Using computer vision and deep learning can achieve automatic waste management. [ABSTRACT FROM AUTHOR]