Internal Defect Detection of Structures Based on Infrared Thermography and Deep Learning

Rapid and accurate detection of internal defects in bridges has always been a major concern of the management and maintenance departments. In the present study, an intelligent method for the detection of structural internal defects is proposed based on infrared thermography and deep learning. Through theoretical analysis, numerical simulations and laboratory experiments, the classification, localization and quantification of internal defects of concrete structures were achieved with the infrared thermography and deep learning method. The mean average precision for classification and localization of internal defects is 96.59%, the mIoU for pixel-level segmentation is 95.19%, and the average relative error for damage quantification is 0.70%. The feasibility of the trained model is verified with new images, and the results show that the trained model can capture infrared thermal features of internal defects with different sizes and depths. This method has the advantages of low cost, high accuracy, easy operation, and large area scanning of concrete structures, which can provide a good reference for the detection of internal defects of concrete structures.