Infrared Thermal Imaging-Based Crack Detection Using Deep Learning

Vision-based approaches are widely used in steel crack detection. After processing the images taken by the camera, the superficial defects can be detected. Due to the common limitation of the nature of photographic images, internal features of objects cannot be fully discovered. In order to overcome the drawbacks of vision-based methods, this work presents an approach for detecting cracks in infrared thermal imaging steel sheets using Convolutional Neural Networks (CNN). Firstly, a horizontal heat conduction method is researched to thermally excite the surface of the steel sheet, and a rolling electric heating device is developed to be a thermal excitation source. Secondly, we analyze the temperature difference between normal area and different crack depths, and study the influence of temperature change. Thirdly, 3000 infrared thermograms labeled for penetrating cracks, non-penetrating cracks, and surface scratches are fabricated into a databank. Then, the CNN is trained and validated on the data bank. Finally, improving the original Faster Region-based Convolutional Neural Network (Faster R-CNN) network, the feature maps of multiple levels in the feature extraction network are aggregated, and the anchor selection scheme of the Region Proposal Network (RPN) is adjusted from 9 to 25. The robustness of the improved Faster R-CNN is demonstrated by evaluating the detection results on the 125 images outside of the data bank, the accuracy and Mean Average Precision (mAP) are 95.54% and 92.41%, respectively, which outperform the original algorithm by increasing the accuracy of 3.18% and mAP of 1.88%.