Foreign Object

Foreign object detection (FOD) focuses on automatically identifying unwanted items in various settings, such as airport runways, railway tracks, and manufactured products. Current research emphasizes developing robust and efficient algorithms, often leveraging deep learning architectures like YOLOv5 and Vision Transformers, sometimes enhanced with attention mechanisms and self-supervised learning techniques, to improve accuracy and reduce computational demands. This is driven by the need for reliable, automated FOD systems to enhance safety and efficiency across diverse industries, particularly where manual inspection is impractical or insufficient. The development of efficient data generation methods, such as using computed tomography for training data creation, is also a key area of focus to address the challenges of data scarcity and annotation costs.