Automated corrosion detection in Oddy test coupons using convolutional neural networks

The Oddy test is an accelerated ageing test used to determine whether a material is appropriate for the storage, transport, or display of museum objects. The levels of corrosion seen on coupons of silver, copper, and lead indicate the material’s safety for use. Although the Oddy test is conducted in heritage institutions around the world, it is often critiqued for a lack of repeatability. Determining the level of corrosion is a manual and subjective process, in which outcomes are affected by differences in individuals’ perceptions and practices. This paper proposes that a more objective evaluation can be obtained by utilising a convolutional neural network (CNN) to locate the metal coupons and classify their corrosion levels. Images provided by the Metropolitan Museum of Art (the Met) were labelled for object detection and used to train a CNN. The CNN correctly identified the metal type and corrosion level of 98% of the coupons in a test set of the Met’s images. Images were also collected from the American Institute for Conservation’s Oddy test wiki page. These images suffered from low image quality and were missing the classification information needed to train the CNN. Experts from cultural heritage institutions evaluated the coupons in the images, but there was a high level of disagreement between expert classifications. Therefore, these images were not used to train the CNN. However, the images proved useful in testing the limitations of the CNN trained on the Met’s data when applied to images of coupons from different Oddy test protocols and photo documentation procedures. This paper presents the effectiveness of the CNN trained on the Met’s data to classify Met and non-Met Oddy test coupons. Finally, this paper proposes the next steps needed to produce a universal CNN-based classification tool. Graphic Abstrac

Minimally Invasive Identification of Degraded Polyester-Urethane Magnetic Tape Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy and Multivariate Statistics

Audio recordings are a significant component of the world’s modern cultural history and are retained for future generations in libraries, archives, and museums. The vast majority of tapes contain polyester-urethane as the magnetic particle binder, the degradation of which threatens the playability and integrity of these often unique recordings. Magnetic tapes with stored historical data are degrading and need to be identified prior to digitization and/or preservation. We demonstrate the successful differentiation of playable and nonplayable quarter-inch audio tapes, allowing the minimally invasive triage of tape collections. Without such a method, recordings are put at risk during playback, which is the current method for identifying degraded tapes. A total of 133 quarter-inch audio tapes were analyzed by attenuated total reflectance Fourier transform-infrared spectroscopy (ATR FT-IR). Classification of IR spectra in regards to tape playability was accomplished using principal component analysis (PCA) followed by quadratic discriminant analysis (QDA) and K-means cluster analysis. The first principal component suggests intensities at the following wavenumbers to be representative of nonplayable tapes: 1730 cm^–1, 1700 cm^–1, 1255 cm^–1, and 1140 cm^–1. QDA and cluster analysis both successfully identified 93.78% of nonplayable tapes in the calibration set and 92.31% of nonplayable tapes in the test set. This application of IR spectra assessed with multivariate statistical analysis offers a path to greatly improve efficiency of audio tape preservation. This rapid, minimally invasive technique shows potential to replace the manual playback test, a potentially destructive technique, ultimately allowing the safe preservation of culturally valuable content