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 compo-nent 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 pa-per can adapt the dataset and automatically search for Backbone components with better performance from the dataset,and effectively im-proves the accuracy and speed of prohibited item detection compared with mainstream algorithms such as FCOS and YOLOv4.
维普
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