# Detection of Safety Signs Using Computer Vision Based on Deep Learning
## 2 Related Works
### Related Research of Computer Vision
The object detection algorithms based on deep convolutional neural networks can be divided into two categories according to detection steps: one is the two-stage object detection algorithm based on candidate regions, and the other is the one-stage object detection algorithm based on regression. The two-stage object detection algorithm first selects the candidate regions of the image, then regression localization and classification of the possible targets in the candidate area, so that the overall detection process is divided into two stages, representative algorithms such as the RCNN series. One-stage object detection algorithms do not select candidate regions separately, omit the candidate region generation step, and integrate feature extraction, object classification, and position regression into one stage, representing algorithms such as YOLO series, SSD series, etc. In 2014, [PERSON] et al. [29] proposed the RCNN model, which firstly extracts approximately 2000 candidate regions using the selective search method [30], secondly obtains the feature map corresponding to each candidate region by convolutional neural networks, and finally classifies the features using support vector machine to distinguish the targets. In 2014, [PERSON] et al. [31] proposed the Spatial Pyramid Pooling Network (SPPNet), which performs feature extraction only once for object detection and uses the SPP layer to adjust the size of the feature map for subsequent detection, avoiding multiple calculations of convolutional features. In 2015, [PERSON] [32] proposed the Fast RCNN model, which improved accuracy by about 12% over the RCNN model on the dataset PASCAL VOC2007 while improving detection speed by about 200 times. Soon after the Fast RCNN model was proposed, [PERSON] et al. [33] proposed the first end-to-end object detection model, Faster RCNN. Faster RCNN uses the regional candidate network to greatly reduce the calculation of extracting candidate regions. In 2016, [PERSON] et al. [34] proposed the first one-stage object detection algorithm, YOLO (You Only Look Once). The YOLO algorithm only needs to extract the feature of the input image once, so the calculation speed of this algorithm is very fast and achieves an average accuracy of over 60% on the dataset PASCAL VOC2007. Later, [PERSON] improved YOLO and proposed the YOLOV2 [35] algorithm, which improved detection speed and accuracy. In order to improve the detection accuracy of small targets, [PERSON] et al. [36] proposed SSD (single-shot multi-box Detector) in 2016. Although the SSD is simple and fast, it still does not achieve the accuracy of the two-stage detection algorithm. To solve this problem, [PERSON] et al. [37] proposed RetinaNet in 2017, improved the cross-entropy loss, and proposed the loss function, Focal Loss, to ensure a balanced distribution of positive and negative samples during the training process, making the algorithm reach the accuracy of two-stage detectors while maintaining a very fast detection speed. With the development of computer vision technology, more and more fields are turning their attention to intelligence and information technology. Although some studies have attempted to use computer vision technology in the field of monitoring, very little studies have focused on public safety. In terms of safety, recent studies have used vision-based image classification and object detection to achieve various safety signs.