# Comparison of Deep Transfer Learning Models for the Quantification of Photoelastic Images
###### Abstract
When using particle models with a reflective photoelastic coating for experiments, like in [PERSON]'s research, the photoelastic responses of different particles may change depending on how well they are attached. Considering the shear strength and viscosity of the adhesive used and checking the photoelastic responses for numerous particle models enhance the consistency of photoelastic responses in individual particle models. Perfect consistency in the production environment of photoelastic particle models, including factors such as cutting methods for the photoelastic sheet, the quantity of adhesive used, the pressure applied during attachment, and curing time, poses inherent challenges. Unless these factors are perfectly controlled, inconsistent photoelastic responses are likely to arise. In addition to these methods for extracting phase variation information [26; 27], Fourier transform methods [28; 29; 30], and twelve-fringe photoelasticity [31; 32], various approaches have been proposed, including recent attempts at analysis using deep learning methods. Researchers in the field of image analysis have recognized the effectiveness of deep learning methods in extracting meaningful information from image data for various purposes, including classification, recognition, and detection. [PERSON] and his research team [33] proposed a method for analyzing fringe patterns based on deep learning methods. Subsequently, [PERSON] and [PERSON] [34] introduced a new approach to secure the large, labeled dataset required for training. On the other hand, [PERSON] and his research team [35] pro posed a dynamic optical elasticity testing method and analyzed patterns changing over time using artificial neural networks. This research team developed a neural network model for analyzing digital photoelastic images in 2022 [36] and presented PhotoelastNet. Additionally, around the same time, [PERSON]'s research team [37] designed and presented a deep convolutional neural network model with an encoder-decoder structure. In this study, we utilized transfer learning models to address the challenge of subtle differences that are difficult to visually confirm in images obtained through reflective photoelastic experiments. Factors such as the cutting method of the photoelastic material, the amount of adhesive used, the applied pressure during attachment, and the curing time contribute to these differences. Transfer learning has the advantage of utilizing knowledge from pre-trained models even when the number of training data is limited. The performance of transfer learning results is significantly affected by the similarity between the problem to be solved and the transfer learning model. We trained various transfer learning models on the same photoelastic response images and compared the results to identify the most suitable transfer learning model for the problem addressed in this research. We used different models, like VGG [38], Inception [39], ResNet [40], DenseNet [41], and EfficientNet [42], to learn from pictures of the photoelastic response taken at different load levels. Subsequently, we compared the validation results of these models. Finally, we evaluated the performance of the models using metrics such as the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). Table 1 is a compilation of abbreviations commonly used in this paper.