Dangerous goods identification based on multi-channel neural network

Abstract

In the current society, there is an increasing demand for dangerous goods identification technology in X-ray images, but at the current stage, most of the identification of dangerous goods in X-ray images still relies on artificial eye recognition. In order to solve this problem, this paper proposes A method for automatically and intelligently identifying dangerous goods in X-ray images based on the transformation of the convolutional neural network. By adding multi-channel convolution and normalization to the convolutional neural network, the target features are extracted to achieve automatic detection of dangerous goods. The purpose of better identification. In the simulation experiment, using the public data set and self-built data set in the X-ray security inspection field, the accuracy of the identification of dangerous goods in the X-ray image was obtained more satisfactory results than the traditional dangerous goods identification. The improved Alex Net network’ s testing accuracy on contraband knives and guns is 8.53% and 11.6% higher than the training accuracy of the original Alex Net network.