Automatic surface crack detection using segmentation-based deep-learning approach

Abstract

The detection of surface cracks or defects in roads, sewage pipes, pavements, shield tunnels lining, etc. has become a promising area of research creating a significant impact on various application domains. Maintenance and monitoring of the manufactured and natural structures are crucial for their correct functioning and integrity. An effective way to predict structural degradation and deterioration is by detecting crack formation, which defines bad health for any artificial or natural structure. Deep learning-based techniques are prominent in accurately identifying the cracks as opposed to the conventional machine learning techniques, which require large processing time and involves handcrafted features. A deep learning-based architecture for automatically detecting and segmenting the cracks in concrete based material surface is presented in the paper. The training and testing results are demonstrated on a newly created dataset consisting of 3000 images of surface cracks. We have manually annotated the cracks present in each image by drawing a bounding box and segmented mask around them. The testing results are evaluated on parameters such as crack detection accuracy, mean average precision rate (mAP), and crack detection speed. To overcome the data collection and labeling barriers, a part of the dataset is made publicly available for research purposes. The training speed and the convergence rate of the proposed algorithm are optimized by using the concept of transfer learning through freezing and unfreezing certain layers of the network. Additionally, the choice of appropriate hyperparameters for improving the model accuracy is discussed in the article. The performance of the model is evaluated against the ground truth labels generated. The AP is reported 74.156% and 93.445% at IoU (Intersection over union) threshold of 0.7 and 0.5 respectively for crack segmentation.