X-ray Dangerous Goods Intelligent Detection Algorithm Research

As an important tool to ensure people's safe travel,X-ray security inspection system has been widely used in airports,stations,subways and other crowded important places.However,the existing security inspection system mainly relies on manual identification,which has the problems of low detection efficiency,high operating cost,and great influence of human factors.Obviously,it cannot meet the intelligent needs of modern X-ray security inspection.In order to realize the automation and intelligence of the security inspection process,the paper uses the convolutional neural network in deep learning to construct an intelligent detection model,design and implement an X-ray dangerous goods intelligent detection system,which can automatically learn the characteristics of dangerous goods and effectively identify them.The thesis first studies the detection algorithm of dangerous goods,uses morphological methods to extract the detection area of the X-ray image,use the correlation between X-ray image color and material information to segment the image material information,and constructs a multi-channel convolutional neural network to extracts the characteristics of dangerous goods,and for the problem of gradient disappearance on the no overlapping border of the loss of Io U,the Complete-Io U loss and the Distance-Io U non-maximum suppression algorithm are used to improve the bounding box regression.Secondly,considering the problem of large volume and large amount of calculation of the dangerous goods detection model obtained by deep convolutional neural network training,the paper mainly studies the principle and application of channel pruning and knowledge distillation model compression algorithm,and combines the advantages of the two to dangerous goods.The detection model is compressed,and the calculation consumption and model volume are reduced on the basis of ensuring the precision of the original model.Finally,the thesis uses the object-oriented method to analyze the functional requirements of the X-ray security inspection system.From the perspective of security personnel and managers,the system architecture and security inspection process are designed.The model update,dangerous goods detection function,image management andsafety management function and interface were verified,which verified the practicability of the detection and model compression algorithm proposed in the paper.The thesis uses deep learning to realize the detection of dangerous goods,breaks through the low efficiency of manual recognition of the existing security inspection system,the uncertainty brought by human factors,and assists security personnel to improve work efficiency.The development of the X-ray security inspection system towards integration,standardization,automation,and intelligence has also laid a certain foundation for the in depth application research of artificial intelligence in the field of security inspection.