Characterizing Robot Vision Solutions for Anomaly Detection in Confined Spaces

We present a framework to experimentally characterize robot vision systems for anomaly detection during inspection of confined spaces, containing key structural and functional elements, such as pipes, cables, columns, and I-beams. The anomalies typically comprise rust patches and corroded sections, and foreign object debris of different kinds such as industrial tools. We first collect our own dataset in a realistic confined space resembling a large ballast tank, using two commodity RGB-D cameras with a large collection of FODs and rust patches of different shapes and sizes. We then employ fine-tuned object detection models to classify the anomalies in the RGB-D images, and the classification performance is used to characterize the effectiveness of the two depth cameras. The results indicate that one of the cameras tends to capture higher-quality depth images for anomaly detection purposes.