Combination of deep learning with representation learning in X-ray prohibited item detection - Rao, Jianghao; Qin, Peng; Zhou, Gaofan; Li, Meihui; Zhang, Jianlin; Bao, Qiliang; Peng, Zhenming (Frontiers in Physics, 2023)
For X-ray inspection detection, the detector converses the collected X- rays from the objects into electrical signals, and then transmitted to the computer for image processing and analysis. From the aspect of digital image procesing, most of detection tasks focus on data processing and transformation to get valuable features, which makes the algorithms more effective. The consistant requirements for speed and accuracy in X-ray prohibited items detection are still not fully satisfied, especially these pictures in special imaging condition. For noisy X-ray images with heavy occlusion, the direct and suitable approach of representation learning is the optimal solution. According to our study, we realized that heterogeneous information fusion from different extraction approaches can be applied effectively on this issue. We proposed two innovation algorithms to extract effective features on X-ray objects which significantly improve the efficiency in the X-ray prohibited items detection. The brief model we proposed fuses the representations learned from the noisy X-ray images, and outper- forms the best model named DOAM-O so far on OPIXray. Furthermore, the attention module we designed to select information of deep learn- ing and representation strengthen the model, considering this, the model still costs shorter time in the processes of both training and inference, which makes that easier to be trained on a lighter computing device.