In the automatic X-ray imaging systems used to identify knives in security check, using the original single shot multibox detector (SSD) algorithm, the shallow feature maps are poorly represented, features of small targets gradually disappear during the training stage, leading to low detection accuracy and poor real-time performance, and the small targets such as the controlled knives in security check are missing and checked out by mistake. To solve this problem, the original SSD was improved in two ways. On the one hand, the SSD-Resnet34 network model was constructed by replacing the basic network VGG16 in the SSD using a ResNet34 network with stronger anti-degradation performance, and the last three layers of the basic network were convolved and a new low-level feature map was created by lightweight network fusion. Part of the extended layer of the network was deconvolved to form a new high-level feature map. On the other hand, jumping connection was adopted to achieve multi-scale feature fusion between the high-level feature map and the low-level feature map. Analysis of test data shows that the improved algorithm demonstrates improved detection speed and detection accuracy of small targets, such as the X-ray image controlled knives. And the algorithm demonstrates improved robustness and high real-time performance. Using the VOC2007+2012 general dataset, the detection accuracy of the improved SSD algorithm is 1.7% higher than that of the SSD algorithm, reaching 80.5%.