Asphalt Pavement Crack Detection Based on Convolutional Neural Network and Infrared Thermography

Two issues exist in the convolutional neural network (CNN) used for asphalt pavement crack detection: balance between accuracy and complexity, and indistinct edges of cracks and asphalt pavement surface. Infrared thermography (IRT) could use the temperature difference between cracks and pavement surface to better distinguish cracks. This work aims to propose a robust crack detection method based on CNN and IRT. An open benchmark dataset was built for crack detection based on three types of images, including visible images, infrared images, and the fusion of visible and infrared images (in short, fusion image). The dataset also considers different conditions and periods, including single, multi, thin, and thick cracks; clean, rough, light, and dark backgrounds, and three periods in a day. Seven CNN segmentation models are trained and evaluated on this dataset. To keep a balance between accuracy and complexity, evaluation metrics (accuracy, and computational and model complexity) are used to have an overall evaluation of models rather than only the accuracy. The results show that the accuracy and predictions of the visible image and fusion image are almost identical for all models, which are much better than that of the infrared image. When the background is rough or cracks are similar to the background, the fusion image is a better choice for crack detection. Compared with the visible image, all segmentation models have a more stable performance for the fusion image. Among segmentation models, Feature Pyramid Networks (FPN) could be the best model because of its high accuracy and low complexity.