# Smart Detection System of Safety Hazards in Industry 5.0
## 3 Related Work and Background
### Object Detection Algorithms  
Object detection is a fundamental computer vision task aiming to identify and localize instances of specific objects within images or video frames. This task demands both classification, where the class of the object must be identified, and localization, where the position of this object within the image must be determined. The rapid developments of Deep Learning techniques, particularly convolutional neural networks (CNNs), which hierarchically learn the relevant features from raw image data, have played a crucial role in revolutionizing the way object detection is developed and performed. Object detection algorithms can be divided into two main approaches, namely two-stage and one-stage methods. Two-stage algorithms, such as R-CNN (region-based CNN) [33] and its advanced variants like Fast R-CNN [34] and Faster R-CNN [35], first propose a series of bounding boxes and then classify them. One-stage methods, like SSD (Single Shot Multibox Detector) [36] and YOLO (You Only Look Once) [37], detect objects in a single pass through the network. Two-stage methods are often more accurate but computationally expensive, while one-stage algorithms are generally faster and, therefore, suitable for real-time applications; however, they sacrifice some accuracy. In the case of real-world applications, in which the inference time is important, YOLO is the preferable choice, allowing faster processing without significant loss of accuracy. Throughout the years, YOLO experienced a series of iterative enhancements, progressing from the initial YOLOv1 [37] to a more sophisticated YOLOv4 [38] and YOLOv5 [39]. Each iteration has introduced improvements in speed, accuracy, and functionality. Object detection is widely used in various applications, including autonomous driving, smart agriculture, healthcare, industrial automation, and safety. Specifically for industrial safety, by employing object detection, workplace safety can be enhanced, preventing accidents and ensuring regulatory compliance. In industrial environments, the capabilities of object detection systems to instantly identify objects are valuable. By continuously monitoring the surroundings, these systems can promptly detect potential hazards such as moving machinery, falling objects, or the absence of PPE. Numerous research studies have extensively explored Deep Learning object detection algorithms to detect PPE in different environmental conditions. Several studies explicitly focus on safety helmet detection in various hazardous environments. The study in [40] presents considerable advantages for detecting safety helmet wear on construction sites using convolutional neural networks for face detection and bounding box regression. However, this work does not consider variations in environmental conditions. The capabilities of YOLOv4 and YOLOv5 are explored for accurate and real-time helmet detection in the construction site [41] and the power industry [42], respectively. The results of those studies indicate that they can meet the requirements of real-world scenarios, but the detection accuracy should be further improved. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Research Paper** & **Domain** & **Task** \\ \hline [PERSON] et al. [24] & Maritime surveillance & Ship recognition in aerial images \\ [PERSON] et al. [25] & Robot scene understanding & Object detection in vending machine \\ [PERSON] et al. [22] & Objects detection for the household environment \\ [PERSON] and [PERSON] [26] & Facilities management & Facility management component object detection \\ [PERSON] et al. [27] & Industry & Dataset for object detection \\ [PERSON] et al. [28] & Urban scene understanding & Semantic Segmentation \\ [PERSON] et al. [29] & & Worker detection \\ [PERSON] et al. [30] & Construction site safety management & Worker detection and tracking \\ [PERSON] and [PERSON] [31] & & Worker fall detection \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research works on synthetic data creation using virtual environments.