# Deep Learning in Left and Right Footprint Image Detection Based on Plantar Pressure
## 3 Results  
### Experimental Results  
In this study, we randomly split 974 plantar pressure images into 70% for the training and 30% for the validation set. According to Table 1 and Figures 5 and 6, our proposed method can detect the foot profiles and classify them with over 60% accuracy without  
Figure 4: The examples of labeling the maximum area of plantar pressure image dataset with a different condition; (**A**) The footprint images with pressure in the full foot. (**B**) Complex footprint images with pressure in the rearfoot and midfoot but loss of pressure in the forefoot. (**C**) The abnormal foot progression angle with high pressure on the forefoot and midfoot and less pressure on the rearfoot. (**D**) Defective footprint images with high pressure on the heels, lost pressure on the midfoot and forefoot.  
overfitting. YOLOv4 has the best comprehensive performance evaluation out of all the models. YOLOv4 was the model that exhibited persistent accuracy gains across various performance evaluations, including \(mAP\) (99.45%), \(P\) (99.00%), \(R\) (100.00%), and \(F1\)_-score_ (99.00%). Meanwhile, ResNet-50 showed the lowest performance in various evaluation metrics, including \(mAP\) (66.00%), \(P\) (66.00%), \(R\) (50.00%), and \(F1\)_-score_ (57.00%). Furthermore, ResNet-50 achieved the lowest \(mAP\) in our study. Moreover, the YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x obtained \(mAP\) values of 91-93%, which were lower than those of YOLOv3 and YOLOv4 (99%).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Model Performance Evaluation**} \\ \cline{2-7}  & \multicolumn{1}{c}{\(AP\)} & \multicolumn{1}{c}{\(mAP\)} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Recall**} & \multicolumn{1}{c}{\(F1\)_-Score_**} \\ \cline{2-7}  & \multicolumn{1}{c}{**Left**} & \multicolumn{1}{c}{**Right**} & & & \\ \hline YOLOv3 & 99.80\% & 99.81\% & 99.80\% & 94.00\% & 99.00\% & 97.00\% \\ YOLOv4 & 99.62\% & 99.28\% & 99.45\% & 99.00\% & 100.00\% & 99.00\% \\ YOLOv5s & 89.80\% & 93.70\% & 91.70\% & 99.58\% & 99.64\% & 100.00\% \\ YOLOv5m & 89.30\% & 95.10\% & 92.18\% & 99.62\% & 99.64\% & 100.00\% \\ YOLOv5l & 91.40\% & 94.60\% & 92.98\% & 99.42\% & 99.64\% & 100.00\% \\ YOLOv5x & 90.20\% & 93.10\% & 91.66\% & 91.84\% & 87.92\% & 89.00\% \\ DenseNet & 90.06\% & 89.17\% & 89.62\% & 56.00\% & 97.00\% & 71.00\% \\ ResNet-50 & 61.88\% & 70.13\% & 66.00\% & 66.00\% & 50.00\% & 57.00\% \\ \hline \hline \end{tabular} Note: \(AP\), Average Precision; \(mAP\), Mean Average Precision.  
\end{table}
Table 1: Performance evaluation of object detection models.  
Figure 5: The average precision of left and right foot detection in different object detection models.  
Figure 6: YOLO series comparison results in various metrics. YOLOv4 exhibits persistent accuracy gains across various metrics.  
The average \(AP\) on different object detection models was 89.01% and 91.86% on the left foot and right foot, respectively. The average \(AP\) on YOLO models was 93.35% on the left foot and 95.93% on the right foot. The right foot showed higher accuracy than the left foot in average \(AP\) in all YOLO series, as well as DenseNet and ResNet-50 models. Furthermore, in the YOLOv3 model, the highest \(AP\) was 99.80% and 99.81% on the left foot and right foot, respectively. However, the ResNet-50 model's lowest \(AP\) was 61.88% and 70.13% on the left foot and right foot, respectively.