SCD-YOLO: A security detection model for X-ray images based on the improved YOLOv5s

X-ray security inspection is widely used in the subway, high-speed rail, airports, key locations, logistics, and other scenarios. However, because of the complexity and diversity of objects in the X-ray images in real-world scenarios, it is easy for security personnel to make mistakes or miss inspections when they are fatigued or not fully focused. In this paper, we proposed an improved model based on YOLOv5 to help security inspectors improve the efficiency of security inspection procedures. First, we replaced the SPP(spatial pyramid pooling) feature fusion module with SPPFCSPC to further enhance the feature extraction capability. Then, we added CoordConv before each feature map input to the detection head. This enables the model to perceive positional information and enhances its feature extraction capability, effectively addressing the detection of small prohibited items in complex backgrounds. Finally, we used decoupled detector head instead of the traditional coupled detector head to separate the classification and localization tasks further improves the detection speed. The experimental results show that our method achieves 77% accuracy. Compared with state-of-the-art methods, our model also achieves significant improvements in detection accuracy and recall.