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A platform for research: civil engineering, architecture and urbanism
Sparse‐sensing and superpixel‐based segmentation model for concrete cracks
Efficient image‐recognition algorithms to classify the pixels accurately are required for the computer‐vision‐based inspection of concrete defects. This study proposes a deep learning‐based model called sparse‐sensing and superpixel‐based segmentation (SSSeg) for accurate and efficient crack segmentation. The model employed a sparse‐sensing‐based encoder and a superpixel‐based decoder and was compared with six state‐of‐the‐art models. An input pipeline of 1231 diverse crack images was specially designed to train and evaluate the models. The results indicated that the SSSeg achieved a good balance between the recognition correctness and completeness and outperformed other models in both accuracy and efficiency. The SSSeg also exhibited good resistance to the interference of surface roughness, dirty stains, and moisture. The increased depth and receptive field of sparse‐sensing units guaranteed the representability; meanwhile, structured sparse characteristics protected the network from overfitting. The lightweight superpixel‐based decoder omitted skip connections, which greatly reduced the computation and memory footprint and enlarged the input size in the inference.
Sparse‐sensing and superpixel‐based segmentation model for concrete cracks
Efficient image‐recognition algorithms to classify the pixels accurately are required for the computer‐vision‐based inspection of concrete defects. This study proposes a deep learning‐based model called sparse‐sensing and superpixel‐based segmentation (SSSeg) for accurate and efficient crack segmentation. The model employed a sparse‐sensing‐based encoder and a superpixel‐based decoder and was compared with six state‐of‐the‐art models. An input pipeline of 1231 diverse crack images was specially designed to train and evaluate the models. The results indicated that the SSSeg achieved a good balance between the recognition correctness and completeness and outperformed other models in both accuracy and efficiency. The SSSeg also exhibited good resistance to the interference of surface roughness, dirty stains, and moisture. The increased depth and receptive field of sparse‐sensing units guaranteed the representability; meanwhile, structured sparse characteristics protected the network from overfitting. The lightweight superpixel‐based decoder omitted skip connections, which greatly reduced the computation and memory footprint and enlarged the input size in the inference.
Sparse‐sensing and superpixel‐based segmentation model for concrete cracks
Xie, Xiongyao (author) / Cai, Jielong (author) / Wang, Haozheng (author) / Wang, Qiang (author) / Xu, Jieying (author) / Zhou, Yingxin (author) / Zhou, Biao (author)
Computer‐Aided Civil and Infrastructure Engineering ; 37 ; 1769-1784
2022-11-01
16 pages
Article (Journal)
Electronic Resource
English
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