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Early and timely defect detection is essential to ensure the safe operation of hydraulic concrete structures. The deep learning-based computer vision method does not require complex manual feature engineering, and can automatically determine the category of structural defects in remote images, overcoming the shortcomings of traditional manual vision that are labor-intensive, subjective and prone to errors. Inspired by this, this paper proposes a deep learning-based defect detection method, which introduces attention mechanism into the ResNeXt50 network to adaptively recalibrate the channel-level feature responses, so that the model can pay more attention to the defect information in the image and enhance the feature extraction ability. Test results show that the proposed method can achieve an F1 score of 88.0%, and realize a good classification effect for common concrete defects.
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早期和及时的缺陷检测对于保证水工混凝土结构的安全运行至关重要。基于深度学习的计算机视觉方法无需复杂的手工特征工程,可以在远程图像中自动判定结构缺陷类别,克服了传统人工视觉劳动强度大、主观性强且易出错的缺点。受此启发,提出了一种基于深度学习的缺陷检测方法,在ResNeXt50网络中引入注意力机制,以自适应地重新校准通道级特征响应,使模型更加关注图像中的缺陷信息,增强特征提取能力。测试结果表明,所提方法可以实现88.0%的F1分数,对于常见混凝土缺陷可以实现较好的分类效果。
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