## 2 Related Works  
At present, several approaches have been investigated for automatic identification of proper PPE use [6; 7; 8; 9; 10; 11], which are divided into sensor-based approaches and vision-based approaches. Sensor-based approaches primarily rely on remote locating and tracking techniques, e.g., radio frequency identification (RFID). [PERSON] et al. [6] designed a RFID-based approach for PPE compliance checking. RFID tags were attached to PPE, and the RFID readers were positioned at the site access. However, only an individual entering the construction site could be checked. [PERSON] et al. [7] developed a real-time location system for worker positioning to determine whether the worker is required to wear a hard hat in this area. A pressure sensor was attached to the hard hat to determine whether it was being worn, and if not, to transmit a warning. Generally, existing sensor-based approaches have difficulty identifying proper PPE use for individuals on the sites. The practical implementation of the tags or sensors may lead to high costs due to the large number of devices required.  
Vision-based approaches are performed by processing images captured by an on-site surveillance camera. Vision-based approaches are nonintrusive and require fewer devices compared to sensor-based approaches because of the widespread application of on-site surveillance cameras. [PERSON] et al. [8] proposed a vision-based approach to detect the edge of objects inside the region of the upper head, i.e., hard hats, using edge detection algorithms. However, this approach relies on the facial features. Therefore, individuals without their full face visible cannot be recognized. [PERSON] et al. [9] introduced a vision-based non-hardhat-use (NHU) detection approach that uses background subtraction and the histogram of oriented gradients (HOG) features to simultaneously detect humans and hard hats in images. The detected human body area and the hard hat area were then matched for NHU detection. However, the hard hat area could not be successfully identified in the case of individuals in various postures (e.g., sitting, bending, and crouching down) or occlusion. Additionally, the approach relied on background subtraction, which makes it unable to detect workers standing at the site without any movement. In general, these approaches rely heavily on hand-crafted features and may consequently fail under complicated conditions with different viewpoints, different individual postures, weather variability, and occlusions, which are very common in construction sites.  
In recent years, deep learning-based object detection methods have shown remarkable performance in visual tasks in the architecture, engineering, and construction (AEC) industry. [PERSON] et al. [10] proposed an automated approach to detect construction workers' NHU based on a two-stage object detection model, Faster R-CNN [12]. The bounding rectangles that surround workers in the image were annotated as the ground truth to train the Faster R-CNN model. The NHU workers were detected, and other regions in the image were identified as the background in the testing phase. [PERSON] et al. [11] deployed Single Shot Multibox Detector (SSD) architecture with reverse progressive attention (RPA) for hard hat detection. A benchmark dataset GDUT-HWD was generated to train the SSD-RPA model. However, existing deep learning-based detection approaches are mainly focused on learning to localize only PPE(s) or NHU individual(s) in the obtained images, which may fail to identify PPE in cases of uncommon human gestures or appearance. Furthermore, almost no research studies have been conducted concerning proper use identifications for multiple PPE, i.e., more than hard hats. In response to these limitations, the scope of this paper is devoted to proposing a novel approach for the identification of proper PPE use and evaluating the effectiveness and robustness of the proposed approach for identifying the proper use of hard hats and full-face masks in the decommissioning of Fukushima Daiichi NPS.