Limited Field Images Concrete Crack Identification Framework Using PCA and Optimized Deep Learning Model

Abstract

Concrete crack identification methods based on machine learning can greatly improve extraction efficiency and precision. However, in many cases, model training requires a large amount of sample data, and insufficient data makes it difficult to effectively obtain model parameters. This study introduces a deep learning framework that integrates filters, principal component analysis, and attention mechanisms suitable for small sample sizes. Firstly, the histogram equalization method is used for the raw images, which can effectively enhance image contrast. Then, to acquire effective images of the crack, different methods are employed for crack detection, which are subsequently handled by principal component analysis (PCA) for optimal feature choice. Att-Unet and Att-Mask R-cnn segmentation models are used to design the detection for concrete cracks. To raise the learning ability of the segmentation models, an attention mechanism is applied to each feature layer of the decoder, and the loss function is evaluated using a combination of the Focal function and Cross Entropy. To verify the effectiveness of the proposed method, Deep Crack datasets and 76 sets of concrete crack data were collected for testing. Experimental results have shown that the method proposed can significantly reduce the model’s demand for data volume and improve training speed, which provides a new direction for small-sample crack extraction.