Transformer And Knowledge Distillation-Based Baggage Threat Detection

Abstract

Effective detection of threats in baggage screening is one of the most important aspects of security, especially in highly trafficked places such as airports, malls, and other public venues. Automated threat detection systems have advantages but are still hampered by very high false alarm rates, inability to handle complex or occluded threat objects, and computational inefficiencies that hinder real-time applications. Additionally, conventional machine learning and deep learning approaches are often faced with the difficulties of catastrophic forgetting, which can severely curtail their capacity to adapt and remain effective while incrementally learning from new datasets.Considering the significant gaps mentioned above, the thrust of this research is to establish a thoroughly competent, very accurate, and computationally efficient automated threat detection system based on transformer-based architecture (SegFormer) with an incremental learning and knowledge distillation approach. Therefore, the objectives of this research are:developing a transformer-based optimized knowledge-distillation approach,reliable detection of critical security threats such as guns,explosives, and hazardous materials, public X-ray image databases having been used for training and testing, and evaluating system performance and real-life usability with full-fledged simulated baggage environments.To enable the model to adapt to new threats using incremental learning. The proposed approach operates with SegFormer, which is a hierarchical transformer capable of effectively capturing global contextual information and thus significantly outperforming simpler architectures, such as Encoder-Decoder and Convolutional Transformer (CST). Incremental learning coupled with a novel knowledge distillation-based loss function allows the model to assimilate newly generated information sequentially, without suffering from catastrophic forgetting, thus maintaining the learned representations for earlier threats while being capable of efficiently adapting to newer ones. Impressive improvement in segmentation efficacy was proved through a rigorous evaluation on three pertinent and well-known X-ray datasets-SIXRAY, GDXRAY, and PIDRAY-while the intersection over union score attained 0.854, 0.6831, and 0.805, respectively. Valuable insights into the model’s interpretability were gained through the application of Grad-CAM and t-distributed stochastic neighbor embedding (tSNE) visualization techniques, which clearly delineated accurate threat localization and feature clustering enhancement through incremental learning and knowledge distillation. This research anchors the foundations laid in automated luggage threat detection, demonstrating much higher levels of accuracy, adaptability, and computational efficiency for near-real-time security applications. This kind of work has practical implications for bolstering security and saving lives, quickly and accurately identifying threats. Areas for further exploration indicated include optimization for real-time deployment, better interpret-ability, hybrid modeling approaches, continuous adaptation to new threats, and greater generalization due to larger training sets.