# Investigation into Recognition Technology of Helmet Wearing Based on HBSYOLOX-s
## 5 Conclusions  
Currently, there are public datasets, such as COCO and VOC(07+12), in the target detection field, which are only annotated with the names of people and other animals to improve the detection accuracy of the generic model. However, there is no collection and labeling for context-specific detection tasks, such as helmet wear detection, so the current public datasets need to be more flexible to meet detection requirements. In this study, we build an HBSYOLOX-s model for helmet-wearing detection that can accurately recognize the helmet-wearing circumstance and improve the flexibility and robustness of the model, as shown below.  
1. This study replaces the Res8 module of DrakNet with the HorNet module using recursive gated convolution to build a more comprehensive feature map. This is performed to extract all essential features from the target. Meanwhile, eliminating the attention mechanism eliminates superfluous model parameters and provides a good foundation for feature fusion.
2. To fully fuse the extracted important features and prevent the loss of semantic information during the fusion process, the original FPN network is replaced with a BiFPN two-way weighted feature fusion network, which assigns higher feature weights to the large amount of helpful feature information obtained in the previous step. It eliminates the issues of false detection in extreme occlusion and the low detection rate of small targets at great distances.
3. On top of the improved model, the original loss function is replaced with SIOU, which further enhances the model's detection accuracy, and the three improved approaches are then integrated to accomplish the task of helmet-wearing detection.  
Ablation experiments demonstrate that the enhanced approach has an mAP of up to 95.5%, which is 5.4% higher than before the enhancement, and a detection speed of up to 41.3 fps/s. Compared with the YOLOX-s and YOLOv5-s models, the network model parameters are more affluent, the network model's flexibility and resilience are improved, and the model suggested in this research has higher average detection accuracy and speed. In conclusion, the HBSYOLOX-s approach is viable and effective for the detection of helmet use. The improved algorithm was tested under extreme weather conditions, such as high temperatures, and all had good detection results, complete as required for practical detection tasks.  
In the future, with the continued development of artificial intelligence algorithms, we will be able to continue studying more outstanding models from the perspective of one-stage target identification algorithms and applying them to helmet-wearing detection jobs.  
Conceptualization, T.G.; data curation, T.G.; investigation, X.Z. and X.Z.; methodology, T.G.; software, T.G.; validation, X.Z.; visualization, T.G.; writing--original draft, T.G.; writing--review and editing, T.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Foundation of the Xinjiang Uygur Autonomous Region under Grant 2022D01C61 and in part by the \"Tianchi Doctoral Program\" Project of the Xinjiang Uygur Autonomous Region under Grant TCBS202046.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.