基于深度学习的裂纹检测方法严重依赖大量的像素级标注信息,为此提出一种基于半监督学习的裂纹检测方法。该方法将多尺度模块引入到裂纹检测的网络模型中,仅利用小部分的像素级标注数据进行全监督训练。对于无标签数据,融合多种显著性区域检测方法生成伪标签,可以减少对像素级标注信息的依赖。在裂纹数据集上对改进网络进行实验验证,并与常用语义分割网络和弱监督实验基准从主观评价、精度、召回率和F1-score的角度进行比较。实验结果表明,改进网络可以有效提升裂纹的识别准确率,提出的半监督训练策略在仅需6.25%像素级标注信息的情况下,能够取得与全监督方法相当的识别精度和召回率。

The crack-detection method based on deep learning relies heavily on a large amount of pixel-level annotation information. Thus, a crack-detection method based on semi-supervized learning is proposed. The proposed method introduces multiscale modules into the network model of crack-detection. It uses only a small part of pixel-level annotation data for fully-supervized training. For the unlabeled data, the fusion of multiple saliency area detection methods to generate pseudolabels can reduce pixel-level reliance on the labeled information. The improved network is experimentally verified on the crack dataset. It is compared with the commonly used semantic segmentation network and weakly supervized experimental benchmarks from the perspective of subjective evaluation, accuracy, recall rate, and F1-score. The experimental results show that the improved network can effectively enhance crack recognition accuracy. The proposed semi-supervized training method can achieve recognition accuracy and recall rate equivalent to the fully-supervized method when only 6.25% pixel-level label information is required.