Pixel-Level crack detection using an attention mechanism

Rui Li, Kefei Xu, Decheng Wu, Zhiqin Zhu
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引用次数: 1

Abstract

This paper proposes a pixel-by-pixel automatic crack detection method ECCrack. The method based on the combination of a channel attention mechanism and fully convolutional neural network. The codec structure is combined with the channel attention mechanism to enhance network’s ability to utilize crack feature information. In the experiment, we verified this method on the road crack data set, and the F1-score reached 92.83, which is better than other methods. Besides, we also conducted ablation experiments on this method to prove the effectiveness of the increased mechanism.
使用注意机制的像素级裂纹检测
提出了一种逐像素的裂纹自动检测方法ECCrack。该方法将通道注意机制与全卷积神经网络相结合。该编解码器结构与信道注意机制相结合,增强了网络对裂缝特征信息的利用能力。在实验中,我们在道路裂缝数据集上验证了该方法,f1得分达到了92.83,优于其他方法。此外,我们还对该方法进行了烧蚀实验,以证明增加机理的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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