# Transfer Learning-Based Object Detection Model for Steel Structure Bolt Fastening Inspection
## 1 Introduction  
To ensure the success of construction projects, it is imperative to incorporate a comprehensive quality management plan that aligns with the construction schedule and budget plan [1]. The inspection of the construction process holds immense significance in maintaining quality control; however, the increasing complexity of modern construction projects has made this task considerably more difficult and hazardous [2]. Moreover, the labor-intensive nature of the construction industry presents additional challenges, as the associated risks directly contribute to a decline in both project quality and productivity [3]. Within this context, the convergence of technologies including artificial intelligence (AI), drones, big data, and the Internet of things (IoT) throughout industries is gaining momentum as a result of Industry 4.0 trends aimed at overcoming low productivity, safety, and quality of construction works. Data that had to be collected manually by the construction site manager can be automatically collected with IoT technology, and large-scale big data can be analyzed using AI algorithms. Regarding inspection work for quality management, the construction progress can be recorded with a camera and analyzed with deep learning algorithm-based computer vision technology. Recently, research and applications for the automation of construction inspection using computer vision technology are gaining momentum due to the outstanding performance of deep learning algorithms such as convolutional neural networks (CNN) [4]. Several studies have demonstrated the applicability of computer vision algorithms for quality management at the construction site by detecting construction objects such as structural damage [5], concrete samples [6], and cropped bolts [7].  
However, previous studies have indicated that the performance of deep learning-based algorithms is significantly influenced by the amount of training data, particularlythe quantity of images used to train the model [8]. Due to the unique nature and variability of construction projects, collecting training images for construction inspection can be both time-consuming and hazardous [9]. This study aims to address the challenges associated with image collection by implementing data augmentation techniques and developing object detection models based on transfer learning. Data augmentation is a technique used to artificially increase dataset size by adjusting the parameters of the image dataset [10]. And transfer learning is a method of reusing a pre-trained model on a new test to improve algorithm performance with small datasets [11].  
Specifically, this study evaluates the performance of three deep learning-based object detection models including Faster RCNN, RetinaNet, and YOLOv3. With collected images of steel frames with bolts, three object detection models are trained and tested for their accuracy and speed performance. As a result of the comparison, the Faster R-CNN model was better in terms of accuracy, and the YOLOv3 model was better in terms of speed. In addition, the applicability of transfer learning to the YOLOv3 model was tested to improve accuracy. The process and results of this case study demonstrate the feasibility of utilizing transfer learning algorithms for bolt fastening inspection tasks, which traditionally rely on visual inspection by human inspectors. This research contributes to overcoming the limitations of labor-intensive inspection activities and has the potential to enhance productivity in quality management.