X-ray Prohibited Items Detection Based on Inverted Bottleneck and Light Convolution Block Attention Module

Abstract

To resolve the problems of position and angle change causing miss and false detection, low accuracy of difficult samples in X-ray luggage images, using YOLOv5 as the baseline, this paper proposes a model by inverted bottleneck and light convolution block attention module for the X-ray prohibited items detection. The inverted bottle-neck design is introduced in the backbone to emphasize the detailed features and improve the model to cope with the large-angle change problem. The light convolution block attention module is used to suppress background interference and reduce model parameter. The Gaussian error linear unit activation function and improved loss function are used to enhance the nonlinear expression ability, increasing the punishment of predicted value to optimize the model??s detection ability for difficult samples. The proposed model is trained and tested on three large public datasets OPIXray, SIXray, and HiXray, resulting in the mAP of 91.9%, 93.4%, and 82.2%, respectively. The results show that the proposed method can effectively solve the problem of angel change in X-ray luggage, indicating its high accuracy and robustness.

Keywords

• x-ray imagery; prohibited items detection; inverted bottleneck; light convolution block attention module (lcbam)