This paper proposes an automatic GUI component detection method for IBM i systems (AS/400). We present a human-annotated dataset consisting of 1,050 system screen images (including 381 Japanese IBM i system screen screenshots). Each image contains multiple components, including text labels, text boxes, options, tables, instructions, keyboards, and command lines. We developed a detection system based on a state-of-the-art deep learning model and evaluated various approaches using our own dataset. Experimental results demonstrate the effectiveness of the dataset in building a system for detecting components on GUI screens. AS400-DET has the potential to automatically detect GUI components on screens and perform automated testing on systems operating through GUI screens.