Defect detection: An improved YOLOX network applied to a replica of “The Birth of Venus” by Botticelli

Early defects in artworks, which are unnoticeable by the naked eye, can evolve in severity if left unattended; therefore, their timely and accurate detection is of vital importance. Infrared-based non-destructive testing (NDT) methods are widely seen as an effective means to detect defects in artworks. However, most existing methods still rely on the human-based judgment of defects after processing the thermal images, which would inevitably lead to opinion bias and inconsistency. Deep-learning algorithms for computer vision are thought adequate to address the situation. On this point, an improved YOLOX algorithm, with the convolutional block attention module (CBAM) mechanism added to the network, is employed to improve the status quo. The method works with thermal imaging data under flash lamps for tempera paintings on canvas. A part of the famous “The Birth of Venus” by Botticelli (∼1485 ca.) was reproduced by a professional restorer, and three different defects were artificially introduced. To overcome the challenge pertaining to having too few defect samples, the dataset is expanded using finite-element simulation methods. The simulated data are used for training, whereas the replica is used for testing. Experimental results show that adding the CBAM mechanism after upsampling and downsampling of the path aggregation network can maximize network performance. The performance of the improved network herein proposed outperforms the benchmarks, with an average accuracy of 96.67%, a detection speed of 20.0 fps, and a total of 5.08 million parameters.