FSPT: Foreground Separation Prompt Tuning for Few-Shot Fine-Grained X-Ray Classification

With the rapid growth of global air traffic and the increasing complexity of security threats, aviation safety has become more critical than ever. As a key component of aerospace and electronic systems infrastructure, X-ray imaging systems are critical for ensuring operational safety in air transportation. Consequently, numerous research works have focused on enhancing object detection performance in X-ray imagery. However, the few-shot fine-grained X-ray item classification (FSFG-XC) tasks have not received sufficient attention. Classifying prohibited items in X-ray screening poses considerable difficulties, mainly because of the high visual similarity among these items in X-ray images. In addition, factors, such as overlapping shapes, shadows, occlusion, and color fading, complicate the distinction between items, especially from different viewing angles. In this work, we propose a foreground separation prompt tuning (FSPT) method to address these challenges. Our method consists of three key components, a prompt-based vision transformer (PVT), an attention-based foreground separation module (AFSM), and an ASCM. The PVT is designed to efficiently adapt tasks by updating only a small number of additional parameters, while keeping the pretrained backbone frozen. Subsequently, the AFSM mitigates background interference by integrating multilayer attention mechanisms. The ASCM enhances local fine-grained feature discrimination through spatial contrast learning. We also introduce our primary X-ray prohibited item dataset, FineXray, which consists of 3955 images across 20 categories. This dataset aims to advance FSFG-XC studies in more challenging scenarios. Results from experiments conducted on the FineXray dataset along with widely used fine-grained benchmarks illustrate the superior efficacy of the proposed FSPT in comparison to current methodologies, offering a robust and efficient solution for enhancing intelligent threat recognition capabilities in aviation-focused electronic systems.