# Transfer Learning-Based Object Detection Model for Steel Structure Bolt Fastening Inspection
## 5 Conclusions  
In the construction industry, performing comprehensive inspections has become increasingly challenging as building projects grow in size and complexity. To address these difficulties, the industry has been embracing computer vision algorithms, in particular, have shown promise in enhancing the accuracy and efficiency of construction inspections. However, the limited collection of sufficient data poses a significant challenge to the applicability of vision-based approaches. Therefore, to address the limitation of data collection on construction sites, this study aims to develop and evaluate deep learning-based object detection models incorporating data augmentation and transfer learning approaches. The focus of this study is on T/S high-tension bolts, commonly used to join steel materials in construction projects. To train the object detection models, an image dataset was collected by searching Internet websites and visiting steel construction sites. The dataset underwent preprocessing to prepare it for training the deep learning algorithms, including Faster R-CNN, RetinaNet, and YOLOv3. The performance of these models was then compared to identify the most suitable algorithm for construction inspections. Notably, there were clear differences in terms of accuracy and speed among the models based on each algorithm. Faster R-CNN demonstrated the highest accuracy, but its object detection speed was too slow for real-time construction inspections. On the other hand, YOLOv3 exhibited the fastest object detection speed but the lowest accuracy among the three algorithms. RetinaNet fell between Faster R-CNN and YOLOv3 in terms of detection accuracy and speed. Given the importance of fast detection speed for real-time construction inspection, this study employed transfer learning to improve the accuracy of the YOLOv3-based model. By leveraging transfer learning, the YOLOv3 model's mean average precision (mAP) value increased by 22.84%, from 49.25% to 72.09%. As a result, a YOLOv3-based object detection model suitable for construction inspections was developed through transfer learning, achieving similar accuracy to RetinaNet even with the smallest inference time. The findings of this study indicate that the proposed object detection model can significantly enhance the efficiency of construction inspections with a small amount of training data. By leveraging the proposed vision-based approaches for checking bolt fastening on steel frames, project managers can conduct inspections with higher accuracy and efficiency compared to traditional visual judgment methods employed by inspectors. Although there are possible benefits of the proposed methodology in this research, there are still limitations to be addressed in further research. This research validated the proposed methodology with bolt fastening inspection on the steel frame. The proposed approach can be utilized for other construction inspection tasks, such as form installation, rebar installation, or finishing works. But it needs additional tasks of data customization, training algorithm, and validation. Hence, it is required to investigate the specific characteristics of inspection work, and to develop a vision-based algorithm for diverse features of image data from construction sites. Moreover, the proposed model can be applied to real construction sites, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Validation of** \\ **Classification** \\ **Loss (\%)** \\ \end{tabular} & \begin{tabular}{c} **Validation of** \\ **Objectness** \\ **Loss (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **mAP (\%)** \\ **loss (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Inference Time** \\ **per Image** \\ **(sec/img)** \\ \end{tabular} \\ \hline YOLOv3 & 3.382 & 0.1811 & 72.09 & 0.0042 \\ (after transfer learning) & 4.372 & 0.6885 & 49.25 & 0.0089 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Algorithm performance comparison after transfer learning.