为了提高卷积神经网络设计的自动化程度并进一步提高复杂背景下违禁品检测的准确率和速度，提出了一种基于神经网络架构搜索的X射线图像违禁品检测算法。首先，设计逐层渐进式搜索策略和多分支搜索空间，并基于批量归一化指标为每一个layer结构搜索最佳侧分支；然后，逐层搜索构建新的骨干网络组件；最后，组成由数据驱动的新目标检测模型。该算法在数据集HiXray、OPIXray、PIDray上分别取得了83.4%、87.2%、70.4%的检测精度。实验结果表明，本文算法能够自适应数据集并自动搜索出性能更好的Backbone组件，与FCOS、YOLOv4等主流算法相比，有效提高了复杂背景下违禁品检测的准确率和速度。

In order to improve the automation of convolutional neural network (CNN) design and further improve the accuracy and speed of prohibited item detection in complex background, a prohibited item detection algorithm for X-ray images was proposed based on neural network architecture search. First, a layer-by-layer progressive search strategy and a multi branch search space were designed, and the best side branches were searched for each layer structure based on batch normalization (BN) metric. Then, the backbone component of the target detection model was constructed based on the layer-by-layer progressive search strategy. Finally, a new data-driven X-ray image prohibited item detection model was formed. The experimental results have demonstrated that the algorithm achieves detection accuracy of 83.4%, 87.2% and 70.4% respectively on three datasets HiXray, OPIXray and PIDray. The algorithm proposed in this paper can adapt the dataset and automatically search for Backbone components with better performance from the dataset, and effectively improves the accuracy and speed of prohibited item detection compared with mainstream algorithms such as FCOS and YOLOv4.