Object Detection in Airport Security Checks

Airport security is a concern in modern travel infrastructure, with the need to identify prohibited items while ensuring passenger safety and minimizing difficulties. Object detection is essential in streamlining this process, assisting security persons in identifying potential threats accurately and efficiently. In this paper, an object detection model based on YOLOv7 is proposed for airport security screenings which uses deep neural network to analyze the images. YOLOv7 is the improved version of YOLO (You Look Only Once). It gives exceptionally good performance in terms of accuracy and speed for object detection in real time scenarios. After evaluating it on standard dataset shows that the proposed system achieves mAP of 82.3 % in accurately identifying forbidden items while lowering down the false warnings.