# An Improved YOLOv5s-Based Helmet Recognition Method for Electric Bikes
## 2 Related Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**Related Work** & **Limitations** \\ \hline [PERSON] et al. (2018) & The initial stage of the method focuses on motorcycle detection. Once the motorcycle is detected, the algorithm continues to determine whether the motorcycle is in motion. Finally, in the last step, the algorithm performs helmet detection, which is a complex process that may result in a relatively slow execution speed. \\ \hline [PERSON] et al. (2019) & The main purpose of this method is to detect worker helmets in construction site scenes. It uses the YOLOv5x algorithm as the benchmark and adopts a large model size. Real-time requirements are not considered in this approach, and it is not designed for recognizing helmets in traffic e-bike scenarios. \\ \hline [PERSON] et al. (2010) & This method consists of multiple stages and has high algorithmic complexity. It does not prioritize real-time requirements in its design. \\ \hline [PERSON] et al. (2011) & A design method for helmet system detection based on TensorFlow is proposed to address the issue of frequent accidents at construction sites. However, this method is not suitable for traffic scenarios. \\ \hline [PERSON] et al. (2012) & A helmet detection method is proposed for factory scenes that combines multi-feature fusion and Support Vector Machines (SVM). However, this method has limited generalization ability due to the manual design of features and is not suitable for detecting helmets in traffic scenes. \\ \hline [PERSON] et al. (2013) & Combining traditional machine learning methods with random forest (RF), an intelligent recognition algorithm based on DCNN and RF is proposed for worker helmet detection. It is not suitable for traffic scenarios. \\ \hline [PERSON] et al. (2014) & The method consists of two steps: the first step uses the modified YOLOv5 detector to detect motorcycles from video surveillance; the second step takes the motorcycles detected in the previous step as input and continues to use the modified YOLOv5 detector to detect whether the motorcyclist is wearing a helmet or not. It is essentially a two-stage approach with high complexity and does not consider real-time requirements. \\ \hline [PERSON] et al. (2015) & The method utilizes a generalized learning system (BLS) optimized by a genetic algorithm as an image recognition classifier for helmet detection. However, the effectiveness of this method in recognizing helmets in traffic scenes is unknown. The system focuses on labeling and reminding construction site workers without helmets in videos rather than specifically targeting traffic scenarios. \\ \hline [PERSON] et al. (2016) & YOLOv3-tiny is used as the baseline algorithm to enhance the performance of the model. However, the resulting model size is still slightly larger than that of YOLOv5s, even with the improvements. Therefore, when deploying an application, a slightly larger storage space is required to accommodate the model. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work and its limitations. Hence, the purpose of this study is to propose a deep learning-based algorithm for electric bike helmet detection, and the aim is to achieve efficient and accurate detection of electric bike helmets, thereby providing support for improving traffic safety.