# Transfer Learning-Based Object Detection Model for Steel Structure Bolt Fastening Inspection
## 2 Literature Review
(2019) utilized YOLOv3, a deep learning-based object detection algorithm, to develop and implement an algorithm that effectively identifies risks by analyzing the situations involving construction machine operators and the surrounding workers during earthwork activities [24]. Similarly, [PERSON] et al. (2019) used a CNN-based construction object detection model to identify collisions at construction sites [25]. [PERSON] et al. (2019) employed YOLOv3 to develop an algorithm that automatically detects whether construction workers are wearing proper safety equipment [26]. In terms of quality control and inspection, which is the field of this research, several studies have focused on utilizing object detection algorithms for quality management, particularly in the detection and classification of damages in constructed structures. For instance, [PERSON] et al. (2019) conducted real-time evaluations of structural safety by detecting surface damages such as cracks, peeling, spalling, and rebar exposure [5]. [PERSON] et al. (2017) employed a hybrid image scanning system mounted on unmanned aerial vehicles (UAVs) to capture image data of concrete samples, using a convolutional neural network (CNN) to detect surface conditions [6]. [PERSON] et al. (2019) utilized a deep learning algorithm based on the regional convolutional neural network (R-CNN) to detect bolt images, enabling the estimation of bolt-loosening angles based on cropped bolt images [7]. [PERSON] et al. (2021) utilized a CNN-based YOLOv3 algorithm for detecting fractured bolts in steel bridges [27]. In addition to construction sites, [PERSON] and [PERSON] (2022) proposed high-resolution and temporal context region-based (HRTC R-CNN) for corrosion defect detection on shipping containers [28]. These studies demonstrate the diverse applications of object detection algorithms for quality management, providing valuable insights into the identification and assessment of structural damages in construction projects. In the computer vision-based approaches that have been introduced so far, large-scale image data collection is essential to train deep learning algorithms. For example, [PERSON] et al. (2017) developed a deep convolutional neural network with 200,000 images of concrete cracks [6], and [PERSON] et al. (2019) used 50,000 images in the CIFAR-10 dataset [29] for training pre-training [7]. [PERSON] and [PERSON] (2022) also collected 10,000 images and separated the dataset into three parts: the training set had 8500 images, the validation set contained 1500 images, and the test set contained 1500 images. Recording video and collecting images manually requires time-consuming efforts. In addition, extensive amounts of image collection also require significant annotation work. Generally, previous studies collected images and annotated specific objects in images with annotation software, such as Labeling, LabelMe, and Amazon Mechanical Turk [30; 31]. Researchers are struggling with extensive amounts of annotation work. For instance, [PERSON] et al. (2016) estimated the annotation time as 11 s per one image [32], and [PERSON] et al. (2018) spent more than 200 h to manually label 7790 images [18]. Overall, the difficulties of image data collection and annotation can be an obstacle to applying vision-based quality management at construction sites.