# Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety
## 2 Materials and Methods  
Several studies have been conducted in the past to detect safety helmets for the workers at construction sites. This literature review focuses on three major techniques used for safety helmet detection: sensor-based, machine learning-based, and deep learning-based detection. A total of 84 papers were found to be relevant for this study, of which 64 were excluded after abstract screening, resulting in the inclusion of 20 papers in this study. These papers present models for detecting safety helmets using the above-mentioned three techniques. Sensor-based techniques usually try to track the safety helmet and worker. [PERSON] et al. [19] combined the Zigbee and Radio Frequency Identification (RFID) technologies to detect personal protective equipment (PPE). The workers wear a microcontroller-based device to detect the PPE and send information to a central unit which will generate an alert if the worker is not wearing PPE properly. Similarly, [PERSON] et al. [1] and [PERSON] et al. [11] used RFID technology to detect PPE at construction sites. [PERSON] et al. [1] designed a mobile-based RFID model that detects workers wearing PPE while passing through the checking gate. [PERSON] et al. [11] created a real time IoT-based smart hard-hat system, which combines RFID technology and Internet of Things (IoT) to detect PPE. However, RFID-based technology cannot confirm that a worker is wearing PPE or safety helmet properly. Furthermore, while sensor-based technology can be a good reliable solution, these methods always rely on external equipment, which makes this technology very difficult to implement, especially on very large construction sites. Moreover, sensor-based methods are expensive [20]. To overcome the problem of device dependence, machine learning-based object detection methods have been widely used. Moreover, these methods offer increased feasibility and higher detection accuracy. [PERSON] et al. [21] used vertical and horizontal projection methods for head segmentation and combines them with the K-Nearest Neighbor (KNN) method for helmet detection. [PERSON] et al. [22] combine two techniques for helmet detection. First, Haar-like features were used to detect the helmet region [23], and then Circle Hough Transform (CHT) was applied to detect half and full helmets. Similarly, [PERSON] et al. [24] proposed an automatic helmet detection method using Histograms of Oriented Gradient (HOG) features to detect workers, and then applying the CHT technique for safety helmet detection. [PERSON] et al. [25] and [PERSON] et al. [26] also used HOG features for segmentation followed by utilizing conventional machine learning methods for detecting safety helmets. [PERSON] et al. [27] used Haar-like features for face detection; then, in order to reduce false positive cases, they detected the motion of the worker, and used color information to detect the helmet. [PERSON] et al. [28] developed a safety framework for workers at construction sites using segmentation and edge detection for detecting safety helmets. [PERSON] et al. [29] in their work first applied a visual background extractor (ViBe) algorithm for the segmentation of moving objects, followed by the C4 algorithm for pedestrian classification, and finally the color feature discrimination (CFD) algorithm for safety helmet detection. [PERSON] et al. [12] used a Microsoft Kinect sensor to collect color and depth data, and then applied machine learning techniques to detect workers and their actions at construction sites. Machine learning-based technologies use hand-crafted features for safety helmet detection that could lead to poor generalization in complex environments like bad weather or at big construction sites. With the recent developments in the area of deep learning-based object detection, many researchers have used deep learning strategies for safety helmet detection. [PERSON] et al. [30] used different YOLO architectures to detect four different color safety helmets, persons, and vests.