# Deep Learning in Left and Right Footprint Image Detection Based on Plantar Pressure
## 4 Discussion  
The deep learning performance evaluation on different object detection models showed our excellent results using plantar pressure images to identify the left and right footprints. YOLOv4 performed well in various metrics such as \(mAP\), \(P\), \(R\), and \(F1\)-\(score\) (Table 1 and Figure 6). Furthermore, the right foot detection achieved a higher average \(AP\) than the left foot detection (Figure 5). Figure 7: The prediction sample of detection in YOLOv4: (**A**) the defective plantar pressure images on the midfoot. (**B**) The complex plantar pressure on the forefoot and midfoot. (**C**) The complex plantar pressure with less pressure at full foot. (**D**) The plantar pressure image on the forefoot with abnormal foot progression angle. The YOLOv4 model showed good performance in detecting the left and right feet. Referring to Table 1. the results of YOLOv4 were balanced over 99.00% in various performance evaluations (\(mAP\), \(P\), \(R\), and \(F1\)-_score_). YOLOv4 showed an \(R\) value of 100.00%, indicating that the detection was similar to the ground truth. Our results were identical to [PERSON] et al. [32]. In their study, YOLOv4 was sorted out, tried all possible optimizations in the entire process, and found the best effect in each permutation and combination, affecting overall performance. As far as we know, YOLOv4 models employ a CSP DarkNet-53 classifier and spatial pyramid pooling (SPP) that connects the YOLOv3 head [33]. YOLOv4 has the advantage of high detection accuracy and precise positioning of the bounding box [18]. The YOLOv4 results indicate that the model may have advantages in multi-color channel imaging detection [34]. Compared with YOLOv4, YOLOv3 was more accurate in grayscale medical images [22]. Furthermore, YOLOv5 is beneficial in sequential imaging such as visual audio or video [35]. Our study aimed to identify the left and right feet; YOLOv4 performed more significantly than YOLOv3 and YOLOv5. Conversely, YOLOv4 has advantages in localizing and classifying multi-object and multi-color channel image features. In our study, YOLOv4, in plantar pressure image detection, showed high performance; this may be the result of plantar pressure images with multi-color features. Our results are similar to [PERSON] et al. [36]. They investigated state-of-the-art object detection systems for detecting traffic sign images using YOLOv3, YOLOv4, ResNet-50, and DenseNet. They found that the YOLOv4 model exhibited higher accuracy due to multi-color imaging. The advantages of YOLOv4's performance were beneficial for predicting the left and right feet in plantar pressure images. On the other hand, DenseNet and ResNet-50 have limitations in object detection. DenseNet has more powerful uses in medical image segmentation [37]. However, plantar pressure is not under medical images, and the DenseNet used for object detection produces a simple transition layer structure of dimensionality reduction. Therefore, it is difficult for a single sensory field to capture multilevel features of the dense block layers [38]. Meanwhile, ResNet-50 may also be influential in medical image categorization using residual networks in the same-sized object on an image [39]. In object detection with plantar pressure, images have more unusual object sizes, and loss of the image features may challenge ResNet-50 networks. Therefore, DenseNet and ResNet-50 may not be suitable models for plantar pressure image detection. The abnormal foot progression angle and complex footprint images complicated the identification of left and right feet via plantar pressure images [18]. Furthermore, plantar pressure images are needed for bounding box annotation to specify the location of the object, which can help recognize the foot features [40]. In this study, detection of the left and the right feet was conducted by deep learning.