Weight-guided dual-direction-fusion feature pyramid network for prohibited item detection in x-ray images

Abstract

Accurate and robust detection of prohibited items in x-ray images has been playing a significant role in protecting public safety. However, large-scale variation of prohibited items and diverse backgrounds in x-ray images bring in many challenges to the detection. We propose an effective weight-guided dual-direction-fusion feature pyramid network (WDFPN), making full use of multilevel features to solve the scale variation problem in cluttered backgrounds. Specifically, our WDFPN mainly consists of weight-guided upsample fusion pathway (WUFP), attention-based connection (AC), and downsample fusion pathway (DFP). WUFP uses channel-wise weights generated from high-level features to weight low-level features, reducing invalid information redundancy. AC transfers enhanced low-level detail information to DFP. Subsequently, DFP improves the localization capacity of the entire features pyramid by the bottom-up fusion pathway. Extensive experiments on the security inspection x-ray and occluded prohibited items x-ray datasets demonstrate the superiority of our WDFPN in detecting prohibited items.