An Enhanced Prohibited Items Recognition Model
This work improves security screening for baggage inspection systems, but it is incremental as it builds on existing methods with specific modifications.
The authors tackled the problem of recognizing prohibited items in X-ray images by addressing the challenge of small-scale items that hinder model performance, achieving a mAP of 89.9% on SIXray10 and 74.8% on another dataset.
We proposed a new modeling method to promote the performance of prohibited items recognition via X-ray image. We analyzed the characteristics of prohibited items and X-ray images. We found the fact that the scales of some items are too small to be recognized which encumber the model performance. Then we adopted a set of data augmentation and modified the model to adapt the field of prohibited items recognition. The Convolutional Block Attention Module(CBAM) and rescoring mechanism has been assembled into the model. By the modification, our model achieved a mAP of 89.9% on SIXray10, mAP of 74.8%.