Feature-Optimized Machine Learning for High-Accuracy Ammunition Detection in X-Ray Security Screening

This paper introduces a machine learning system that is feature-optimized to enhance the detection of concealed ammunition in X-Ray security imaging. The system integrates advanced image analysis techniques with a cascade-AdaBoost classifier and Multi-scale Block Local Binary Pattern (MB-LBP) features, which are particularly effective for object recognition and classification in complex, high-dimensional data. The combination of these algorithms ensures robust performance in identifying ammunition types even under challenging conditions, such as variations in image quality or object orientation. The system is specifically designed for the accurate identification of various types of ammunition, including 9 mm bullets for handguns, AK-47 machine gun bullets, and 12-gauge shotgun cartridges. To support the development and testing of this system, a new dataset comprising 1,732 X-Ray images of passenger luggage was collected. This dataset is made publicly available to facilitate further research and improvement in this critical area of security technology. Experimental results demonstrate that the system achieves a high level of detection accuracy, with the ability to identify 12-gauge shotgun shells concealed in baggage with a 92% success rate. Beyond its technical achievements, this system significantly enhances the efficiency and reliability of security checks, improving the overall effectiveness of ammunition detection in real-world scenarios.