# Detection of Safety Signs Using Computer Vision Based on Deep Learning
## 2 Related Works
### Computer Vision in Safety Applications  
In recent years, many models for testing the wearing of safety helmets and other protective equipment have been proposed. However, very little research on safety signs detection. This section briefly introduces the application of two kinds of target detection algorithms in the safety field.  
[PERSON] et al. [38] proposed a CNN coal and rock identification method based on hyperspectral data. Because of the dangerous behavior of many workers without safety helmets, [PERSON] et al. [39] proposed an improved Faster RCNN algorithm to detect the wearing of safety helmets, Retinex image enhancement technology was used to improve image quality, and the K-means++ algorithm was used to better adapt to small safety helmets, the average accuracy of the improved algorithm was increased by 8.1%, effectively overcoming the interference of light, distance, and other factors. [PERSON] et al. [40] proposed a method for automatic helmet detection based on the YOLOV3 deep neural network, CSPNet was used to replace the Darknet53 backbone network, SPP structure was added, and a top-down feature fusion strategy was implemented to improve the multi-scale prediction network to achieve feature enhancement. The detection speed was improved by 28% over the original YOLOV3, and the speed was increased by 6 fps. In 2020, [PERSON] et al. [41] proposed a YOLOV4 object detection network. [PERSON] et al. [42] effectively distinguished between helmet wearers and non-wearers and achieved real-time detection of helmet-wearing. Although the application of the unimproved YOLOV4 model has a good detection effect, it is not improved for specific cases. One of the critical issues was poor performance and weak robustness for object detection tasks. [PERSON] et al. [43] proposed a helmet detection method based on the improved YOLOV4; the K-means algorithm was used to cluster the dataset to obtain more targeted edge information, and the multi-scale training strategy was also used to improve the adaptability of the model to different scale detection. After the model was improved, the detection accuracy and detection speed were improved compared with YOLOV4. [PERSON] et al. [44] proposed a fusion attention network based on the YOLOV4-tiny detection algorithm, and an atrous spatial pyramid pooling network (ASPPNet) was introduced after the feature extraction was completed by the lightweight backbone network CSPDarkNet-tiny of YOLOv4-tiny, the problem of detection speed and high-altitude seat belt feature extraction was solved. Since the SSD model is also a one-stage object detection model with the advantages of easy deployment and fast detection results, some researchers have applied it to fire detection tasks; [PERSON] et al. [45] focused on real-time detection, early fire detection, and false detection in fire detection and compared and analyzed the classic monitoring methods to detect forest fires: Faster R-CNN, YOLO, and SSD, among them SSD have better real-time performance, higher detection accuracy, and early fire detection capability. [PERSON] et al. [37] proposed RetinaNet in 2017, improved cross-entropy Loss, and proposed a Focal Loss function to ensure a balanced distribution of positive and negative samples in the training process. The detection accuracy of the algorithm can reach the accuracy of the two-stage detector while maintaining a fast detection speed. Although object detection algorithms have been rapidly developed, well-researched, and applied in many fields [14; 15; 16], they have only just started in the field of safety. Therefore, this paper proposes a model based on YOLOV4-tiny to detect safety signs in various places, laying a solid foundation for AI-based predictions and warnings to reduce the loss and prevent accidents.