X-ray security inspection images classification combined octave convolution and bidirectional GRU

Due to the disadvantages of low accuracy and slow speed in the active vision security inspection method, it is not suitable for real-time security inspection. Aiming at this problem, we propose an x-ray inspection image classification algorithm combining octave convolution (OctConv) with attention-based bidirectional Gate Recurrent Unit (GRU). Firstly, OctConv is introduced to replace the traditional convolution operation to divide the input feature vector into high and low frequency, and reduce the resolution of low frequency features, effectively extracting the features of security image and reducing the spatial redundancy. Then, the feature weight can be adjusted by dynamic learning through attention-based bidirectional GRU to improve the classification accuracy of threat objects. Finally, a lot of experimental results on SIXRay dataset show that the classification accuracy, AUC value and PRE of 8000 test samples are 98.73%, 91.39% and 85.44%, respectively, with a time of 36.80 seconds. Compared with the current mainstream model, the proposed algorithm can improve the performance and speed of threat objects recognition in X-ray security images.