To detect prohibited items in X-ray images, this study proposed a one-stage dual-network object detection algorithm based on deep learning. Based on the one-stage object detection algorithm Yolov3 and combined with the idea of a composite backbone network, a Yolo-C object detection network is developed. The backbone of Yolo-C (DarkNet-C) consists of an assistant backbone network (Darknet-A) and a lead backbone network (Darknet-L). Each feature layer of the DarkNet-A is cascaded by feature with the upper feature level corresponding to DarkNet-L and then propagated to the next feature level. Finally, a feature map representing image information is obtained. The feature enhancement block (FAB) is introduced to improve detection performance of small object. Feature fusion is performed on the cascaded feature maps to enhance the nonlinear expression ability of features and achieve the purpose of feature smoothing. Besides, transfer learning and data enhancement was adopted to train the network and improve its robustness. The mAP in the SIXray_OD dataset is 73.68%, and detection speed is 40 frame·s ^-1. In the X-ray image detection field, Yolo-C has effectively improved the detection accuracy of different prohibited items and met the real-time requirements of detection.