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- W3125486593 abstract "With the continuous development of pattern recognition technology especially the development of convolutional neural network (CNN), the applications of defect monitoring in substations are widely utilized in industrial circumstances. However, current image analytic methods based on CNN achieved low accuracy and poor robustness due to different scales, non-rigid and complex background of defects. In order to solve these problems, a multi-scale attention networks (MAN) for substation equipment image defect detection is proposed in this paper. In our approach, we firstly design an attention module to extract the global and local features over depth and spatial position activation levels and then three columns attention networks are developed for extracting multi-scale features of defects. Finally, the multi-scale feature maps are fused by using fully connected neural network. The experimental results show that our approach achieves satisfactory results and outweighs the state-of-the-art methods in our defect dataset." @default.
- W3125486593 created "2021-02-01" @default.
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- W3125486593 date "2021-01-01" @default.
- W3125486593 modified "2023-10-18" @default.
- W3125486593 title "A Multi-scale Attention Networks for Substation Equipment Image Defect Detection" @default.
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- W3125486593 doi "https://doi.org/10.1007/978-981-15-9746-6_17" @default.
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