Improving Threat Detection in Airport Security Inspections with X-ray Image Enhancement

Security at airports is critically dependent on the accurate detection of potential threats through X-ray image analysis. However, traditional methods can be hindered by issues such as image noise, poor contrast, and resolution limitations, which impact the effectiveness of threat detection. To address these challenges, this project focuses on enhancing threat detection by applying advanced X-ray image processing techniques combined with deep learning. Specifically, the project employs the MobileNet algorithm, a cutting-edge convolutional neural network to enhance the classification and object identification identification of objects in X-ray images. MobileNet's lightweight architecture and robust performance in image analysis make it an ideal choice for this task, enabling detailed and efficient processing of Xray images. The model is trained on a diverse dataset of X-ray images, capturing a wide range of potential threats and objects. By improving the clarity and detail of X-ray images and leveraging MobileNet's advanced detection capabilities, the system aims to enhance the accuracy and reliability of threat identification. The performance of the MobileNetbased approach is rigorously tested and compared to existing methods, demonstrating significant improvements in detection accuracy and efficiency. This innovative solution aims to support security personnel in making more informed and timely decisions, ultimately contributing to enhanced airport security and passenger safety.