Automatic Pixel-Level Crack Detection for Civil Infrastructure Using Unet plus plus and Deep Transfer Learning

In the practical applications of crack detection, traditional image processing based detection methods are faced with a lot of challenges, such as low level of intelligence, poor adaptability and so on. In this paper, an automatic pixel-level crack detection method based on deep transfer learning is proposed for engineering applications. The proposed detection method is composed of two stages: crack recognition and crack semantic segmentation, which makes it easy to meet the needs of efficient and reliable detection for large-scale collected images. The fine-tuned Vgg16 model in the first stage can accurately identify the crack images and avoid the computing cost for non-crack images in the further processing. The Unet++ model in the second stage provides a pixel-level semantic segmentation for crack images. Multifold knowledge based transfer learning and weighted loss function are proposed to improve the performance and generalization ability of the models. On 5 publicly available datasets, the proposed method achieved a mIoU of 84.62%. Tested on totally 8064 images with a resolution of 224 x 224, the proposed method achieved a mean detection speed of 8.1ms per image. Experiment results exhibit that the proposed method can effectively detect the pixel-level cracks. It provides an end-to-end deep learning method for large-scale crack detection for civil infrastructure. For the crack detection in complex scenes and the improvement of processing speed will be our next in-depth study.

Automated summary

This paper proposes an automatic pixel-level crack detection method based on deep transfer learning, which includes crack recognition and semantic segmentation stages, achieving efficient and reliable detection results. Experimental results show that the method can effectively detect pixel-level cracks and perform well in large-scale crack detection tasks.