| Summary: | The increasing use of high-pressure hydrogen gas has heightened the need to understand material behavior in hydrogen-rich environments. Recent studies have shown that examining the pore-shaped damage in the cross-section of rubber materials exposed to high-pressure hydrogen can provide valuable insights into their resistance to such environments. This paper introduces an approach for training a deep learning model to detect hydrogen-induced pore-shaped damage. The study proposes a semi-automated labeling method and employs a modified faster R-CNN, implementing ResNet50-D, aspectual anchor box optimization, and dataset augmentation. To conduct the testing to validate the proposed method, acrylonitrile butadiene rubber was exposed to hydrogen at 96.6 MPa for 24 h. The dataset was created by analyzing damaged cross-sections using a scanning electron microscope. The detection results demonstrate that the proposed method outperforms both traditional and data-based conventional methods.
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