# ME-YOLO: Improved YOLOv5 for Detecting Medical Personal Protective Equipment
## 4 Experiments and Results
### Analysis of Ablation Experiments  
To further analyze the impact of different improvement methods on the performance of the YOLOv5 algorithm, three sets of experiments were designed to analyze the different improvement methods. In these experiments, precision (P), recall (R), average precision (AP), mean average precision(mAP), and F1-score were used as the evaluation metrics of experiments, as shown in Table 6, where \"\(\surd\)\" indicates that the improvement method is introduced in the model and \"\(\times\)\" indicates that it is not introduced in the model.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**C3\_FFM**} & \multirow{2}{*}{**USEM**} & \multirow{2}{*}{**EloU**} & \multicolumn{6}{c}{**AP (\%)**} & \multirow{2}{*}{**P (\%)**} & \multirow{2}{*}{**R (\%)**} & \multirow{2}{*}{**mAP (\%)**} & \multirow{2}{*}{**GFLOPS**} \\ \cline{5-16}  & & & & & & & & & & & & \\ \cline{5-16}  & & & & & & & & & & & & \\ \hline YOLOv5s & \(\times\) & \(\times\) & \(\times\) & 95.7 & 94.8 & 92.6 & 92.7 & 95.5 & 95.5 & 90.1 & 94.2 & 15.9 \\ YOLOv5s & \(\surd\) & \(\times\) & \(\times\) & 95.6 & 94.9 & 95.3 & 95.9 & 96.6 & 96.4 & 93.4 & 95.7 & 16.8 \\ YOLOv5s & \(\surd\) & \(\surd\) & \(\times\) & 99.0 & 95.0 & 95.5 & 96.5 & 96.5 & 97.0 & 94.3 & 96.5 & 17.5 \\ YOLOv5s & \(\surd\) & \(\surd\) & \(\surd\) & 99.1 & 95.9 & 96.0 & 97.0 & 97.7 & 97.7 & 94.4 & 97.2 & 17.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experiments.  
Figure 12: Visual comparison between YOLOv5s and ME-YOLO. (a) YOLOv5s; (b) ME-YOLO.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **One Image Test Time(s)** & **All Reasoning Time(s)** & **FPS (Frames\(\cdot\)s\({}^{-1}\))** \\ \hline YOLOv3 & 0.047 & 23.5 & 21 \\ YOLOv4 & 0.059 & 29.5 & 17 \\ YOLOv5s & 0.022 & 11.0 & 46 \\ YOLOv5m & 0.026 & 13.0 & 38 \\ YOLOv5l & 0.037 & 18.5 & 27 \\ ME-YOLO & 0.023 & 11.5 & 42 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different models in FPS.  
As shown in Table 6, C3_FFM can fully merge the semantic information of deep feature maps with the localization information of shallow feature maps, improving the adaptability of the backbone network to different sizes of medical personal protective equipment and its ability to extract features. Compared with the original YOLOv5s algorithm, the mAP increased by 1.5%, and the AP of the class \"mask\" increased significantly. In addition, GFLOPS increased by 1. Based on the above, the up-sampling enhancement module (USEM) was introduced, which can fully retain the semantic information and global features of the up-sampled feature map. Compared with the first experiment, the mAP increased by 0.8%, and the AP of the class \"suit\" increased significantly. The third experiment introduced EIoU loss as the loss function of the border regression, solving the problem of slow convergence of the regression of the prediction frame in the original loss function. Based on the aforementioned two experiments, the precision, recall, and mAP of the improved model were 97.7%, 94.4%, and 97.2%, respectively, i.e., 2.2%, 4.3%, and 3.0% better than the original YOLOv5s algorithm, respectively. The GFLOPS was 17.5--slightly higher than that of the original YOLOv5s algorithm.