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- W3120479903 abstract "Rock recognition is a critical step for intelligent control system in tunnel boring machine (TBM). We build the rock recognition dataset by the visual monitoring system in TBM. After that, we present an attention-fusion network (AFN) for rock recognition classification. Our model is trained with the recognition object (rock) image, and gets the rock attention map through the self-intersection operation. To complete the classification task, we propose the attention block in the attention-fusion classifier, which is effective to fuse and encode the feature and attention in an end-to-end manner. We confirm the effectiveness of the self-intersection operation and attention block by visualized experiment and contrast experiment. At the same time, experimental results of the rock recognition dataset indicate that AFN outperforms state-of-the-art methods, including LSTM (mask-guided), ABN (attention-based), ResNetSO (global-feature), both in accuracy and realtime on the rock recognition tasks." @default.
- W3120479903 created "2021-01-18" @default.
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- W3120479903 date "2020-12-13" @default.
- W3120479903 modified "2023-10-16" @default.
- W3120479903 title "Self-intersection Attention Pooling Based Classification for Rock Recognition" @default.
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- W3120479903 doi "https://doi.org/10.1109/icarcv50220.2020.9305305" @default.
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