## 4 Conclusions  
In this study, we investigated the model calibration for accurately detecting asbestos fibers in PCM images using machine learning. A CNN was used for the feature extraction part of the model, and an FC or SVM was used as the classifier. Models were constructed using various hyperparameters to determine the optimal model configuration, and the performance of each model was compared. The models were trained and validated using PCM images of samples obtained from actual demolition sites of ACM buildings. The validation results revealed the following:  
1. The model configuration and hyperparameters affected the model performance. Therefore, the FC's number of layers and nodes and the SVM's kernel function type and constants should be set appropriately. 2. The type of classifier (FC or SVM) affected the model performance; SVM had a higher F1 score and better overall model performance. Conversely, FC was safer because fewer asbestos fibers were missed owing to fewer recalls. 3. Fine-tuning of the feature extraction part improved model performance. 4. Class weighting slightly improved model performance when FC was used as the classifier. When an SVM was used as the classifier, class weighting had minimal impact and, in some cases, slightly degraded the model performance. 5. If overall model performance is essential, it is recommended to use SVM as the classifier without class weighting and with fine-tuning as well as a linear kernel for the SVM kernel function. If the emphasis is on minimizing asbestos fiber misses, it is preferable to use double FCs as the classifier with class weighting and fine-tuning. These results quantitatively clarify the impact of model configuration on model performance and provide insights for engineers and researchers in effectively detecting asbestos fibers. The model developed in this study to assist human visual asbestos detection would greatly reduce human labor. Future research should further improve the accuracy of the model and compare it to conventional methods to achieve fully automated asbestos detection. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14209408/s1](https://www.mdpi.com/article/10.3390/app14209408/s1)]([https://www.mdpi.com/article/10.3390/app14209408/s1](https://www.mdpi.com/article/10.3390/app14209408/s1)), Figure S1: Model performance (F1 score) for each dataset of the models with class weighting without fine-tuning; Figure S2: Model performance (F1 score) for each dataset of the models without class weighting with fine-tuning; Figure S3: Model performance (F1 score) for each dataset of the models without class weighting and fine-tuning; Figure S4: Model performance (Recall) for each dataset of the models with class weighting without fine-tuning; Figure S6: Model performance (Recall) for each dataset of the models without class weighting with fine-tuning; Figure S7: Model performance (Recall) for each dataset of the models without class weighting and fine-tuning; Figure S8: Examples of the results of asbestos detection; Table S1:The hyperparameters to be optimized and the range of exploration; Table S2:The result of hyperparameter optimization of double-layer FC model; Table S3 The result of hyperparameter optimization of SVM with class weighting and fine-tuning; Table S4 The result of hyperparameter optimization of SVM with class weighting but no fine-tuning; Table S5 The result of hyperparameter optimization of SVM without class weighting and fine-tuning. **Author Contributions:** Conceptualization, T.M. and A.K. ; methodology, T.M. ; software, T.M. ; validation, T.M. ; formal analysis, T.M. ; investigation, T.M. ; resources, M.T., S.T. and A.F. ; data curation, M.T., S.T. and A.F. ; writing--original draft preparation, T.M. ; writing--review and editing, H.S. and A.K. ; visualization, T.M. ; supervision, H.S. and A.K. ; project administration, A.K. ; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript.