# Detection of Safety Signs Using Computer Vision Based on Deep Learning
## 2 Related Works  
### Related Research of Computer Vision  
Nowadays, the supervision of mines and some coal mines still relies on manual inspection and assessment, although electronic technology such as camera monitoring equipment has been added to the traditional model to achieve the goal of replacing manual safety inspections. In general, the safety control at coal mines is not automated and visualized enough, so some scholars have turned their attention to information, digital, and intelligent safety management. The progress in the field of machine learning and computer vision-based technology means that there is a new research interest in automatic detection methods and early warning systems in the safety field [21]. Computer vision is the science of how to use machines to \"see\" by processing captured images or videos to obtain three-dimensional information of the corresponding scene. With the development of sensor technology and the introduction of intelligent coal mines, the application areas of computer vision are gradually becoming more widespread. In recent years, many researchers and scholars have introduced computer vision technology in the field of safety to achieve intelligent management of personnel, machinery, and materials in the workplace in order to reduce the occurrence of safety accidents and maximize safety intelligence. For the detection of workers in coal mines, a method is proposed to detect and track the position of modeled, moving objects in real time based on data obtained from video cameras [22]. [PERSON] et al. [23] collected and processed the location data of workers and mine equipment in real-time through visual detection technology and displayed the relevant safety and activity performance information to decision-makers in real-time and visually so as to realize the safety management of workers. [PERSON] et al. [24] developed a new model of cyber-physical. The model enables real-time monitoring of the wearing of personal protective equipment by installing sensors on workers' clothing, using RFID to detect the use of protective equipment by workers, and using Zigbee for network communication. However, these methods require specific and expensive hardware to obtain the original image. At the same time, it also requires a high level of cooperation among workers and takes into account factors such as physical health, personal privacy, and mine convenience. Therefore, with the development of computer hardware and the increase in computer processing speed, deep learning models have been developed, with CNN being the most widely used of the deep learning methods, which has been widely applied in a series of tasks such as image classification and object detection. Image classification is a very important task in computer vision, which can recognize the objects in the image. AlexNet [25] is the first image classification model to be achieved commercially. It is designed with a dual-GPU design model in order to improve the running speed. It also adopts techniques such as local response normalization, dropout, and pooling with overlap to prevent network overfitting. The VGG network [26] was proposed in 2014 by the Department of Science Engineering of Oxford University and uses small-sized convolutions to reduce the number of parameters. Based on the AlexNet framework, the depth of the network layers has been broken. GoogLeNet [27] is a breakthrough deep network model proposed by Google, which uses convolution windows of different sizes to form the Inception module. In order to prevent the excessive training gradient loss after the deep network deepening and situations such as difficult network optimization and slower training, ResNet [28] allows the network to be made very deep by connecting a high-speed channel between the output and input so that shallow features can be passed on. Object detection is fundamental to computer vision because it aims to recognize the semantic features of objects and the positions contained in images.