## 2 Related Work
### Studies Applying Deep Learning Algorithms to Safety Management  
Deep learning is a sub-field of artificial intelligence, involving a model composed of multiple layers of neural networks and is characterized by a powerful learning performance for abstract representations of data. It has achieved the best performances in various fields such as object detection, natural language processing, speech recognition, and time series forecasting [19]. In particular, convolutional neural network (CNN)-based models are widely used in image classification and object detection. In 2012, a DCNN-based model in the ImageNet large-scale visual recognition challenge attracted considerable attention as it achieved tremendous performance improvement compared to the existing model [20]. Since 2015, when DCNN-based models such as ResNet and GoogLeNet were suggested, performances of the models have surpassed human cognitive abilities. Deep learning algorithms are gradually being applied to various fields based on their excellent performance. Some studies applied deep learning in construction safety management. As a human factor-related study, [PERSON] et al. (2018) used faster RCNN, a modified model of CNN, to recognize workers from construction site images and quickly recognize whether they were wearing a safety helmet or not. It achieved high performance with a misclassification rate of about 5% [21]. [PERSON] et al. (2020) recorded high performance using the YOLO-v3 model, which is a real-time object detection model, for multi-label classification of workers, safety helmets, and vests [22]. There was a study combining CNN and other deep learning algorithms. [PERSON] et al. (2018) recognized the unsafe behavior of construction site workers through a hybrid deep learning model that combined CNN and long short-term memory, and showed excellent model performance, surpassing the performance of the existing state-of-the-art models by 10% [23]. [PERSON] et al. (2018) detected construction work objects with a CNN-based feature extractor and identified the relationship among the detected objects using network analysis [24]. It was successful at extracting various activity relation patterns that reflected the interactions between workers, materials, and equipment, but it did not focus on safety factor detection. While the above studies focused on human factors, studies on material factors have also been conducted. [PERSON], [PERSON], and [PERSON] (2018) detected the safety handrail using transfer learning with a VGG-16 model and demonstrated a high accuracy of 96.5% [25]. In a study on detection of construction equipment such as excavators and concrete mixer trucks, [PERSON] et al. (2018) recorded a detection accuracy of 95% using a region-based fully convolutional network model proposed by [PERSON] et al. (2016), while [PERSON] and [PERSON] (2021) proposed YOLO-v3-based model detecting construction equipment and excavator activities (digging, loading, swinging) [26; 27; 28; 29]. [PERSON] et al. achieved fast inference time and good detecting performance using MobileNet-based transfer learning. In another interesting study, [PERSON] et al. (2019) performed tracking and prediction of workers' past movement routes while simultaneously detecting excavators and workers [30]. The safety status of workers was diagnosed through the analyzed spatial-temporal relationship between the worker trajectory and the danger zone derived by equipment such as an excavator. As mentioned above, deep learning models have been applied to safety management studies and showed good results. However, there are practical difficulties in applying these models to actual construction sites as a single framework because most studies have only focused on specific risk factors. Although [PERSON]'s (2020) study included relatively various risk factor labels, this methodology cannot conclusively reduce accidents when applied to construction sites because it mainly focused on classifying and identifying accident narratives by analyzing accident reports [31].