Abstract: To tackle the inefficiencies, high miss-detection rates, and poor mobility of conventional security check methods in identifying contraband hidden on vehicle undercarriage surfaces, a cloud-based mobile intelligent underbody concealment detection system has been devised. This system uses robotics, network communication, image processing, and object recognition technologies to enable intelligent inspection. Employing the SIFT(Scale-Invariant Feature Transform) algorithm for feature information extraction from images, the system attains a panoramic view of the vehicle underside. Four categories of undercarriage concealments are detected using the YOLOv5(You Only Look Once version 5) deep neural network model. A cloud database is constructed to archive vehicle data, with TCP/ IP(Transmission Control Protocol/ Internet Protocol) protocol facilitating seamless interactions among the wheeled robot, the supervisory computer, and the cloud database. Preliminary testing confirms the system’s capability to conduct underbody concealment inspections within designated contexts, achieving 76.7% success rate in image stitching and 87.2% precision in target detection. Hence, it has considerable practical value for vehicle underbody security check applications.

Key words: scale-invariant feature transform(SIFT), image stitching, you only look once version 5(YOLOv5) model, target detection, cloud database