A significant issue in X-ray security inspection is that there are considerably more background images than foreground images. Although convolutional neural network (CNN)-based detection models have proven to be effective at detecting objects in X-ray security inspection systems, they are not designed to handle imbalance issues during training. Hence, typical CNN models are suboptimal at extracting background features and balancing the foreground and background. To address these two problems, we propose an efficient background learning (EBL) method with three modules: mixed foreground and background learning (MFB), hierarchical balanced hard negative example (HBHE) sampler and prime background mining with voting (PBMV). The MFB module extracts the foreground and background during each iteration and combines them into a single image for training, achieving unified and balanced foreground and background training. The HBHE sampler balances difficult foreground and background samples, dynamically choosing the number of negative samples based on the MFB image by calculating the difficulty factor. The PBMV module selects a prime background that is prone to false detection and provides increased attention through multiple voting during training. Experiments show that EBL can reduce false positives while maintaining high recall. When applied to Faster R-CNN, AP(50) increases by 5.8% on benchmark X-ray datasets, including 2.3% on OPIXray and 3.7% on SIXray. Moreover, the performance of our model is improved without increasing computational costs or memory during the inference phase.
