# An Improved YOLOv5s-Based Helmet Recognition Method for Electric Bikes
## 5 Experiment
### Comparative Experiments  
To further validate the effectiveness of the algorithm, experiments were conducted using the same dataset, equipment, and training strategies under the same conditions. Mean Average Precision at IoU 0.5 (mAP.5), model size, and Frames Per Second (FPS) were used as evaluation indicators. These indicators were compared with the Faster-RCNN, YOLOv3, and YOLOv algorithms, and the results are presented in Table 5.  
Based on the comparative experimental results in the table, the performance of the improved electric bicycle helmet recognition method based on the YOLOv5s algorithm compared to other algorithms was analyzed and discussed. The following is an analysis and discussion of the results:  
Firstly, the proposed method was compared with Faster-RCNN, YOLOv3, and YOLOx algorithms in terms of target categories (electric bicycle, helmeted, and non-helmeted) and overall mAP.5.  
From Table 5, it can be observed that Faster-RCNN obtained mAP.5 values of 94.49, 12.92, and 13.29 in the categories E-bike, helmeted, and non-helmeted, respectively, with a total mAP.5 of 40.29. YOLOv3 obtained mAP.5 values of 85.4, 80.7, and 73.7, with a total mAP.5 of 79.9. YOLOx obtained mAP.5 values of 90.91, 90.86, and 90.16, with a total mAP.5 of 90.64. In comparison, the method proposed in this paper performed very well in these categories, generating values of 99, 97.6, and 92.1, with a total mAP.5 of 96.2. These results highlight the significant advantages of the proposed algorithm for electric bicycle helmet recognition.  
Furthermore, each algorithm was evaluated in terms of Frames Per Second (FPS) and model size (storage space). The proposed method achieved an FPS of 35.707, significantly surpassing Faster-RCNN, YOLOv3, and YOLOx. Therefore, the proposed method demonstrates significant advantages in processing speed.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{**Val\_mAP.5/\%**} & \multicolumn{5}{c}{**Test\_mAP.5/\%**} & \multirow{2}{*}{**FPS**} & \multirow{2}{*}{**Volume/mb**} \\ \cline{2-2} \cline{6-11}  & **E-Bike** & & & & & & & & & \\ \hline Faster-RCNN & 94.731 & 12.82 & 14.37 & 40.62 & 94.49 & 12.92 & 13.48 & 40.29 & 15.257 & 113.5 \\ YOLOv3 & 85.7 & 81 & 73.9 & 80.2 & 85.4 & 80.7 & 73.7 & 79.9 & 29.4 & 123.5 \\ YOLOx & 90.85 & 90.58 & 89.12 & 90.18 & 90.91 & 90.86 & 90.16 & 90.64 & 32.072 & 71.8 \\ Last & 99.1 & 97.5 & 92.2 & 96.3 & 99 & 97.6 & 92.1 & 96.2 & 35.707 & 13.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison experiments.  
In terms of model size, the proposed method only occupies 13.8 MB, while Faster-RCNN, YOLOv3, and YOLOv occupy 113.5 MB, 123.5 MB, and 71.8 MB of capacity, respectively. This result indicates that the proposed method has lower storage space requirements.  
In conclusion, the YOLOv5s-based electric bicycle helmet recognition method outperforms Faster-RCNN, YOLOv3, and YOLOv algorithms in terms of target categories and overall mAP.5. In addition, the proposed algorithm also demonstrates advantages in processing speed and storage space. These results highlight the enormous potential of our method in the field of electric bicycle helmet recognition, providing convincing evidence for its practical application.