An automated detection model of threat objects for X-Ray baggage inspection based on modified encoder-decoder model

Modern security faces challenges in detecting unauthorized and potentially harmful items in luggage, despite X-ray baggage scanning frameworks and research on efficiently screening highly disguised items. In response to this gap, a groundbreaking Modified Encoder-Decoder-based model has been introduced. This innovative model takes X-ray scan images as input and generates distinct feature representations for both suspicious and non-suspicious baggage materials. A key focus of the model is to address the denoising challenge inherent in X-ray images which reduces the models efficiency. This is achieved through the implementation of a Poisson Noise Reduction method during the preprocessing stage. Following preprocessing, the model effectively segments the non-threat image, identifying potential threats from the denoised input. The model showcases superior performance, as evidenced by high Peak Signal-to-Noise Ratio (PSNR) and low Mean Squared Error (MSE) values, outperforming existing filtering techniques. Rigorous testing on publicly available SIXray and GDXray datasets validates the effectiveness of the proposed methodology. Performance metrics for the SIXray dataset, including mAP, IoU, and DC values of 97.32%, 73.14%, and 85.12%, respectively, underscore the model's efficacy. Notably, the framework attains an impressive accuracy of 99.17% on the SIXray dataset, affirming its robustness in addressing contemporary security challenges.