Change Detection in Image Pairs for Plant Inspection Using Mobile Robot

In this study, we propose a system to detect changes in three-dimensional (3D) space for autonomous plant visual inspection by a mobile robot. The videos captured by a mobile robot during past inspections are compared with the videos obtained during the current inspection using both pose information and the acquired images. To ensure robustness against changes in shooting conditions, change detection is executed employing deep learning techniques. Subsequently, the detected information is projected onto a 3D space to localize the changes. To verify the effectiveness of the proposed method, experiments were conducted both in a real plant environment and a simulated indoor plant environment. The results of the outdoor experiments showed that the proposed system achieved image pair determination, change detection, and integration into a 3D space. The results of the indoor experiments and evaluations confirmed that the proposed method for image pair determination was suitable based on considerations of detection accuracy and computation time.