# Detection of Safety Signs Using Computer Vision Based on Deep Learning
## 6 Conclusions and Future Work  
Three feasible improvement strategies, including the attention mechanism, the soft-NMS, and the Focal Loss, were proposed for the YOLOV4-tiny algorithm to address the issues of long training time and inaccurate localization of small targets.  
This paper proposed a method applied in the field of safety signs target detection based on the YOLOV4-tiny algorithm and improved it. First, the attention mechanism is introduced, and then the Soft-NMS algorithm and Focal Loss function are introduced. The experimental results indicate that various attention mechanism modules exhibit distinct precision and speed in safety sign detection, with the ECANet module demonstrating the most favorable performance. Furthermore, substituting the traditional NMS algorithm with the Soft NMS algorithm and incorporating the Focal LOSS function contribute to enhanced detection performance. This leads to a reduction in the model's false detection rate, accompanied by a decrease in the variety of false detections. On this basis, the improved model in this paper is compared with the traditional YOLOV4-tiny model before the improvement and the two-stage representative algorithm Faster RCNN. It is concluded that the model detection performance in this paper is the best, with mAP reaching 97.76%, which was 7.55% and 9.23% higher compared to the YOLOV4-tiny and the Faster RCNN algorithms, respectively. At the same time, the detection speed FPS was 1.59, which was significantly improved by 0.38 compared with the two-stage detection algorithm Faster RCNN. At the same time, the over-fitting phenomenon in YOLOV4-tiny and Faster RCNN does not exist in the improved model training process. The improved model has good capability to detect both normal-sized and smaller-sized targets, as well as blurred safety signs in dim light. Overall, the proposed model significantly improved the performance of safety signs detection.  
The algorithm in this paper has limitations for detecting safety sign images with overlapping occlusion objects and datasets. Further work will expand more datasets, collecting other categories of safety signs and covering more detection tasks in the safety field, such as unsafe behavior and dangerous goods, in order to improve the applicability and generalization of the model. Object detection is extensively employed in various fields, such as safety production and surveillance. Therefore, the next step is to consider improving the speed of the model further.  
Formal analysis, Investigation, Writing--original draft, Y.W.; Investigation, Writing--revision, L.Z.; Writing--revision, Supervision, Funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Youth Talent Program of Shaan'xi Province, Natural Science Foundation of Shaan'xi Province [No. 2023-JC-YB-432], and Key Research and Development Plan of Xinjiang Uygur Autonomous Region [No. 2022B03025-2 and No. 2022B03031-1]. And the APC was funded by no funder.  
Not applicable.  
Figure 13: Evolution of the loss function with epoch for (**a**) the YOLOV4-tiny, (**b**) the Faster RCNN, and (**c**) the Improved YOLOV4-tiny models.