# NTS-YOLO: A Notcurnal Traffic Sign Detection Method Based on Improved YOLOv5
## 4 Conclusions  
This study improved the nighttime traffic sign recognition network to enhance its performance in complex nighttime scenarios. Firstly, we introduced the CBAM attention mechanism and the OTA loss function based on the YOLOv5 model to address the challenges of recognizing traffic signs in nighttime environments caused by insufficient lighting, target blurriness, and occlusion. The experimental results show that through the unsupervised nighttime image enhancement method, this paper successfully improved the illumination balance and reduced the background noise level of the dataset, which significantly improved the recognition accuracy of traffic signs.  
Secondly, after introducing the CBAM attention mechanism, the recognition accuracy of the model in the nighttime environment was improved by 0.056%. Meanwhile, the application of the OTA loss function resulted in a 0.052% improvement in the target detection accuracy. However, the introduction of the additional attention mechanism and the optimized loss function resulted in a slight decrease of 8.4% in the model processing speed. Nevertheless, this performance degradation is acceptable considering the significant improvement in the recognition accuracy. In practical applications, the relationship between accuracy and the processing speed needs to be weighed to achieve optimal performance.  
However, it is worth noting that the dataset used in this study was primarily based on Chinese traffic signs, which to some extent limits the model's applicability in international scenarios. Chinese traffic signs exhibit regional characteristics in terms of their shape, color, and text symbols. For example, red circles are used for prohibition signs, and blue rectangles are used for instruction signs. In contrast, traffic signs in other countries may differ significantly in design. For instance, Europe uses white signs with red borders widely, while North America predominantly adopts yellow diamond-shaped warning signs. These design differences may lead to a decline in the recognition accuracy when the model is applied to cross-country scenarios, thereby limiting its applicability in international traffic sign detection tasks.  
In the future, to address the limitations of this study and improve the model, the following directions will be pursued:  
1. Further optimize the computational complexity of the CBAM and OTA modules. By adopting lightweight attention mechanisms or sparsification optimization strategies, the additional computational load can be reduced to improve the inference speed.
2. Expand the internationalization of the training dataset. By incorporating diverse datasets that encompass traffic sign characteristics from more countries (e.g., GT-SRB and LISA), the model's cross-regional applicability can be enhanced, thereby improving its generalization ability.  
Conceptualization, Y.H. and M.G.; methodology, Y.H. and M.G.; software, Y.H. and M.G.; validation, Y.H., M.G., Y.Z. and J.X.; formal analysis, Y.Z., M.G. and Y.H.; investigation, Y.Z., M.G. and Y.H.; resources, Y.Z., M.G. and Y.H.; data curation, [PERSON], M.G. and X.G.; writing--original draft preparation, [PERSON], X.G., T.Z. and R.D.; writing--review and editing, [PERSON], M.G., [PERSON] and J.X.; visualization, M.G., Y.Z. and J.X.; supervision, [PERSON] and Y.Z.; project administration, [PERSON] and M.G.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources (Grant No.KF-2022-07-012).  
Not applicable.  
**Data Availability Statement:** Sequence data supporting the results of this study have been deposited in figshare with primary access via [[https://doi.org/10.6084/m9.figshare.25816276.v1](https://doi.org/10.6084/m9.figshare.25816276.v1)]([https://doi.org/10.6084/m9.figshare.25816276.v1](https://doi.org/10.6084/m9.figshare.25816276.v1)).