# Real-Time Hand Gesture Recognition Based on Deep Learning YOLOv3 Model
## 5 Discussion  
Real-time hand gesture recognition based on deep learning models has critical roles in many applications due to being one of the most advanced domains, in which the computer vision and artificial intelligence methods have helped to improve communication with deaf people, but also to support the gesture-based signaling systems]. In this study, we experimented with hand gesture recognition using YOLOv3 and DarkNet-53 deep learning network models [47]. The dataset, which we used, was collected, labeled, and trained by \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & **Learning** & \multirow{2}{*}{**Images**} & **Precision** & \multirow{2}{*}{**Recall (\%)**} & **F-1 Score** & **Accuracy** \\  & **Rate** & & **(\%)** & & **(\%)** & **(\%)** \\ \hline VGG16 & 0.001 & 216 & 93.45 & 87.45 & 90.3 & 85.68 \\ SGD & 0.1 & 216 & 70.95 & 98.37 & 82.4 & 77.98 \\ SSD & 0.0001 & 216 & 91.25 & 84.30 & 87.6 & 82.00 \\ YOLOv3 & 0.001 & 216 & 94.88 & 98.68 & 96.7 & 97.68 \\ \hline \hline \end{tabular}
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Table 4: Comparison of the accuracy results for the deep learning models used for hand gesture recognition. Best results are shown in bold. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Reference**} & **Model** & **Dataset** & **Accuracy (\%)** \\ \hline [PERSON] et al. [28] & Labeling Algorithm & \multirow{2}{*}{1300 images} & \multirow{2}{*}{93\%} \\  & Producing Palm Mask & & \\  & Particle Swarm Movement & & & \\ [PERSON] et al. [31] & (PSO), Longest Common & & \\  & Subsequence (LCS) & & \\  & Random Forest (RF) and & & \\ [PERSON] et al. [32] & Boosting Algorithms, Decision & \multirow{2}{*}{7805 gestures frames} & \multirow{2}{*}{63\%} \\  & Tree Algorithm & & \\  & Model Induction Algorithm, & & \\ [PERSON] et al. [50] & K-star Algorithm, Updated & & \\  & Naive Bayes Algorithm, & & \\  & Decision Tree Algorithm & & \\  & AdaBoost Algorithm, SAMME & & \\ [PERSON] et al. [51] & Algorithm, SGD Algorithm, & & \\  & Edgebox Algorithm & & \\  & FEMD Algorithm, Finger & & \\ [PERSON] et al. [52] & Detection Algorithm, & & \\  & Skeleton-Based Matching & & \\ Proposed model & YOLOv3 & & 216 images (Train) & \\  & & & \\ \hline \hline \end{tabular}
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Table 5: Comparison of the state-of-the-art works with our proposed model. ourselves. We compared the performance of YOLOv3 with several other state-of-the-art algorithms, which can be seen in Table 5. The achieved results were good, as YOLOv3 achieved better results when compared to other state-of-the art algorithms. However, our proposed approach was not tested on the YOLO-LITE model. The YOLOv3 model was trained on a YOLO-labeled dataset with DarkNet-53. YOLOv3 has more tightness when it comes to bounding boxes and generally is more accurate than YOLO-LITE [53]. Moreover, the aim of our study was not to apply real-time hand gestures on limited computing power devices, since we collected hand images of different size and used different angles for diversity. Hence, we focused only on performance criteria rather than YOLO-LITE [54] criteria to recognize hand gestures of speed. Complex applications such as communication with deaf people, gesture-based signaling systems [55], sign language recognition [4], special signal languages used in sports [56], human action recognition [7], posture detection [57], physical exercise monitoring [10], and controlling smart homes for assisted living [58], are where GPUs could perform better through YOLOv3. In future work we can apply mixed YOLOv3-LITE [59] on our datasets and new datasets of 1-10 numbers for all kind of applications for GPU and non-GPU based computers to achieve real-time object detection precisely and quickly. Furthermore, we can have enhanced our images through oversampling and real-time augmentation [60] as well.