An Enhanced Prohibited Items Recognition Model

TL;DR

This paper introduces an improved model for recognizing prohibited items in X-ray images, utilizing data augmentation, CBAM, and rescoring to enhance detection accuracy, achieving high mAP scores on benchmark datasets.

Contribution

The paper presents a novel model that combines data augmentation, CBAM, and rescoring mechanisms to improve prohibited items recognition in X-ray images.

Findings

Achieved 89.9% mAP on SIXray10 dataset

Improved detection of small-scale items

Enhanced model performance through targeted modifications

Abstract

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%.