The automatic surface defect detection system supports the real-time surface defect detection by reducing the information and high-lighting the critical defect regions for high level image under-standing. However, the...
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The automatic surface defect detection system supports the real-time surface defect detection by reducing the information and high-lighting the critical defect regions for high level image under-standing. However, the defects exhibit low contrast, different textures and geometric structures, and several defects making the surface defect detection more difficult. In this paper, a pixel-wise detection framework based on convolutional neural network (CNN) for strip steel surface defect detection is proposed. First we extract the salient features by a pre-trained backbone network. Secondly, contextual weighting module, with different convolutional kernels, is used to extract multi-scale context features to achieve overall defect perception. Finally, the cross integrate is employed to make the full use of these context information and decoded the information to realize feature information complementation. The experimental results of this study demonstrate that the proposed method outperforms against the previous state-of-the-art methods on strip steel surface defect dataset (MAE:0.0396;F???: 0.8485).
Concrete cracks pose significant challenges to infrastructure maintenance and safety. Traditional methods for detecting cracks suffer from inefficiency and subjectivity. Deep learning has shown promise recently, yet i...
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Concrete cracks pose significant challenges to infrastructure maintenance and safety. Traditional methods for detecting cracks suffer from inefficiency and subjectivity. Deep learning has shown promise recently, yet it often requires extensive labeled data. A semi-supervised concrete crack detection network (SS-CCDN) is proposed to handle the aforementioned challenge. To be specific, a multi-task model that takes edge feature detection of cracks as an auxiliary task is established. This model is then applied to both student and teacher networks to realize semi-supervised detection. Furthermore, the feature fusion networks incorporate the innovative multi-head cosine non-local attention module to achieve comprehensive acquisition and implementation of feature information at different scales. Lastly, the memory module is integrated into the network to compare the commonalities and distinctions between input sample information and memory sample data, successfully and effectively detecting concrete crack areas. SS-CCDN outperforms other baselines in detection, according to experimental results on two benchmark datasets.
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