Assisting airport security screening through YOLO techniques and generating results using LIME interpretation

With thousands of passengers travelling through airports on a daily basis during air travel, it is particularly important to ensure that the lives of passengers and crew are safe. In modern airport security screening, it is vital to ensure the efficient and accurate detection of threats in passenger baggage, which will have a bearing on whether or not the safety of company and individual property can be effectively safeguarded. In addition, failure to effectively improve the accuracy and efficiency of threat item detection during airport security screening may waste security resources and increase passenger inconvenience, leading to anxiety and dissatisfaction during screening and affecting their travelling experience. Furthermore, if the detection technology is not effective in screening threatening items, airports may need to increase the number of security personnel to manually check baggage, which can significantly increase operational costs. This research therefore utilises YOLOv5 object detection technology to improve the efficiency of airport security systems. YOLOv5's real-time capabilities can quickly identify potential threats by detecting prohibited items in baggage X-ray images. However, the black-box nature of deep learning models such as YOLO often prevents the transparency required for critical security applications. To address this issue, LIME (Local Interpretable Model- agnostic Explanations) was integrated into the workflow to provide interpretable visualisations that shed light on the decision-making process of YOLO models. By applying LIME, we generated visual interpretations that highlighted key areas of the image that influence model predictions, leading to a deeper understanding of model behaviour and increased trust in the system. The experimental results show that the accuracy of detecting Seal increased to 0.64, indicating a significant improvement in the model's ability to detect this category. The accuracy rate reaches ${1 . 0 0}$ at a confidence level of 0.936, implying that almost all predictions are correct when the model's prediction confidence level exceeds 0.936.The YOLO-LIME combined framework not only maintains high detection accuracy, but also significantly improves the interpretability of the model's decisions. This dual approach allows security personnel to better understand and validate model outputs, ultimately contributing to the reliability and transparency of airport security processes.