Automatic Crack Localization and Shape Extraction Using Convolutional Block Attention Module and Score-CAM

Aging concrete structures pose growing risks to public safety and the resilience of infrastructure systems. This situation highlights the importance of detecting damage at an early stage and performing maintenance in a timely manner. In Japan, many infrastructure facilities have been in service for more than 50 years. As a result, there is an urgent need to improve the efficiency of inspections and reduce their associated costs. Recent advances in deep learning offer potential for automating infrastructure inspections. However, these methods typically require a large amount of labeled data. In particular, the annotation process requires expert knowledge and involves significant time and cost, which creates a major obstacle to practical application. In this study, we present an efficient annotation method for detecting cracks in concrete surfaces. The method uses a convolutional neural network for classification, which is improved by incorporating the Convolutional Block Attention Module (CBAM) and Score-CAM. CBAM enhances the model's ability to extract features related to cracks. In addition, Score-CAM is applied to the layers at both early and later stages of the network. The feature maps from these layers are then combined to improve the stability of the predictions and to achieve high accuracy in identifying cracks at the pixel level.