Gesture recognition based on modified Yolov5s

IET Image ProcessingVolume 16, Issue 8 p. 2124-2132 ORIGINAL RESEARCHOpen Access Gesture recognition based on modified Yolov5s Dunli Hu, Dunli Hu Beijing Key Laboratory of Field-Bus Technology and Automation, Beijing, 100144 People's Republic of China College of Electrical and Control Engineering, North China University of Technology, Beijing, 100144 People's Republic of ChinaSearch for more papers by this authorJun Zhu, Corresponding Author Jun Zhu 15836998534@163.com orcid.org/0000-0002-1148-849X Beijing Key Laboratory of Field-Bus Technology and Automation, Beijing, 100144 People's Republic of China College of Electrical and Control Engineering, North China University of Technology, Beijing, 100144 People's Republic of China Correspondence Jun Zhu, College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, People's Republic of China. Email: 15836998534@163.comSearch for more papers by this authorJiayu Liu, Jiayu Liu College of Electrical and Control Engineering, North China University of Technology, Beijing, 100144 People's Republic of ChinaSearch for more papers by this authorJiaju Wang, Jiaju Wang College of Electrical and Control Engineering, North China University of Technology, Beijing, 100144 People's Republic of ChinaSearch for more papers by this authorXiaoping Zhang, Xiaoping Zhang Beijing Key Laboratory of Field-Bus Technology and Automation, Beijing, 100144 People's Republic of China College of Electrical and Control Engineering, North China University of Technology, Beijing, 100144 People's Republic of ChinaSearch for more papers by this author Dunli Hu, Dunli Hu Beijing Key Laboratory of Field-Bus Technology and Automation, Beijing, 100144 People's Republic of China College of Electrical and Control Engineering, North China University of Technology, Beijing, 100144 People's Republic of ChinaSearch for more papers by this authorJun Zhu, Corresponding Author Jun Zhu 15836998534@163.com orcid.org/0000-0002-1148-849X Beijing Key Laboratory of Field-Bus Technology and Automation, Beijing, 100144 People's Republic of China College of Electrical and Control Engineering, North China University of Technology, Beijing, 100144 People's Republic of China Correspondence Jun Zhu, College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, People's Republic of China. Email: 15836998534@163.comSearch for more papers by this authorJiayu Liu, Jiayu Liu College of Electrical and Control Engineering, North China University of Technology, Beijing, 100144 People's Republic of ChinaSearch for more papers by this authorJiaju Wang, Jiaju Wang College of Electrical and Control Engineering, North China University of Technology, Beijing, 100144 People's Republic of ChinaSearch for more papers by this authorXiaoping Zhang, Xiaoping Zhang Beijing Key Laboratory of Field-Bus Technology and Automation, Beijing, 100144 People's Republic of China College of Electrical and Control Engineering, North China University of Technology, Beijing, 100144 People's Republic of ChinaSearch for more papers by this author First published: 18 March 2022 https://doi.org/10.1049/ipr2.12477AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract With the development of artificial intelligence technology, human–computer interaction technology through gestures, images and voices has gradually become a hot topic for discussion. A modified Yolov5s gesture recognition method is proposed in the field of human–computer cooperation by optimizing the network structure of Yolov5s backbone, CNN is replaced by Ghostbottleneck module to increase the target occlusion recognition rate. Secondly, tensor stitching is added to the output of Ghostbottleneck module for up sampling to strengthen the reuse of image features. Finally, the detection ability of the improved model in the face of complex environment is verified on the self-made data set. Experimental results show that, the mAP@0.5 (mean average precision) of the modified Yolov5s is 94.49%, the AP (average precision) is 94.2%. By comparing the Yolov5s algorithm, Yolov4 algorithm,Yolov3 algorithm and SSD algorithm, the detection accuracy of the modified method has been significantly improved, which can fully meet the application requirements of real-time detection of gesture-controlled robots. 1 INTRODUCTION Gesture is an important part of human–computer interaction and has been widely used in all corners of life, such as household entertainment games with hand-controlled characters, intelligent driving of hand-controlled vehicles, and expression of deaf and mute etc. Thus, hand gesture recognition is particularly significant. At present, common gesture interaction systems can be roughly divided into gesture interaction systems based on sensing devices, signals and visions. However, gesture interaction systems based on sensing devices often require users to wear complex physical devices, which is inconvenient to carry in practical applications. Besides, the increase of the number of sensors will cause additional cost. The vision-based gesture interaction system is greatly influenced by the environment, and its detection speed is slow, so it is difficult to put it into practical application. Gesture recognition method based on deep learning has higher robustness and can automatically learn features without manual intervention. Since those applications are usually built on the embedded platform, the gesture recognition tasks need to be carried out in real-time with limited computational resources (such as GPU, memory etc). The main task of gesture recognition is to locate gesture regions and classify them in images, and its difficulties lie in the non-rigid and semantic similarity of the gestures, the complexity of the background, and the variability of the illumination etc. [1]. Therefore, in the limited data, accurate and fast extraction of gesture features is the key of gesture recognition. Therefore, many workers have studied this problem. Guodong Yin [2] proposes a model to detect human gestures based on the improved Yolov3 network, three DenseNet blocks are added before the residual components in the Yolov3 network to enhance the convolutional feature propagation. Shuai Yuan et al. [3] propose a gesture recognition model based on the combination of improved the Yolov3 network and the Bayes classifier. Abdullah Mujahid [15] proposes a lightweight model based on YOLOv3 and DarkNet-53 convolutional neural networks for gesture recognition, The proposed model was evaluated on a labelled dataset of hand gestures in both Pascal VOC and YOLO format and the precision is 94.88%. Du Xuwei [4] proposed a new Yolov4 model combined with Kalman filter [13] real-time hand tracking method was proposed.The new algorithm can address some problems existing in hand tracking technology such as detection speed, accuracy and stability. Tasnim Ferdous Dima [5] using Yolov5 recognises American sign language. Fangbo Zhou et al. [6] propose a safety helmet detection method based on Yolov5. The proposed Yolov5 [7] based (pre-trained) solution is extremely lightweight and fast to recognize the gesture. Good accuracy and the fps are two important things to consider when working on real-life scenarios. It interprets the gesture of the hand. For this approach, it does not need to wear any special devices. Any gesture capturing tools (i.e. the phone camera) is enough to capture the gesture and then a pre-trained model will be able to recognize it. The proposed pre-trained model is a very light. So, it is possible to deploy it on any mobile device and then use it to interpret the sign that each gesture represents in real-time [5]. When we use the trained Yolov5s model for gesture recognition, we find some problems, as shown in Figure 1. We find that the trained Yolov5s model has the problems of missing and false detection of gestures during real-time detection, especially in dark light, the gesture recognition rate is very low. Therefore, this paper improves the Yolov5s model in combination with the detection requirements of gesture recognition. In summary, this paper has the following contributions: making and collecting 10,000 gesture data sets and labelling them. We use the most advanced target detection algorithm to detect gestures and achieve good results. We trained and evaluated different target detection models (Yolov3, Yolov4 [14], Yolov5, SSD) and compared them. A modified Yolov5s algorithm is proposed to solve the problems of gesture recognition errors and missed detection caused by complex background, uneven illumination and gesture occlusion. By replacing the GhostBottleneck module with the original Yolov5s network framework, the richness of features has increased, and tensor stitching is used in BackBone to increase the reuse of features and enhance the learning ability of the network, so as to adapt to the problem of low gesture recognition rate caused by environment, illumination and similar gestures, and further improve the model accuracy. FIGURE 1Open in figure viewerPowerPoint The wrong detections of the Yolov5s model 2 YOLO ALGORITHM MODEL 2.1 Introduction of Yolov5s algorithm The structural framework of yolov5s is shown in Figure 2. Yolov5s network is divided into four parts: Input, Backbone, Neck and Prediction. The Input port adopts Mosaic Data Augmentation to concat the input pictures in the way of random scaling, random clipping and random arrangement. At the same time, the adaptive anchor box calculation is carried out. The prediction box is output on the basis of the initial anchor box, and then compared with the real box ground truth to calculate the gap between the two, and then the reverse update is carried out to iterate the network parameters. Finally, the adaptive image scaling is carried out, and the original image is uniformly scaled to a standard size, and then sent to the detection network, as shown in Figure 3. FIGURE 2Open in figure viewerPowerPoint The structural framework of Yolov5s FIGURE 3Open in figure viewerPowerPoint Mosaic data augmentation In the backbone, the input image with mage resolution of 640 × 640 × 3 passes through the focus structure. Using the slicing operation, it becomes 320 × 320 × 12 feature mapping, and then through the convolution operation of 32 convolution kernels, it finally becomes 320 × 320 × 32 feature mapping [6]. CSP module mainly carries out feature extraction on the feature map. As shown in Figure 4, CSP cross stage local fusion features enhance CNN's learning ability, maintain accuracy while lightweight, reduce computing bottleneck and reduce memory cost. FIGURE 4Open in figure viewerPowerPoint CSP module The structure of FPN + PAN is adopted in Neck, as shown in Figure 5. FPN fuses the high-level features from top to bottom through up sampling and low-level features to obtain the predicted feature map, and transmits the deep semantic features to the shallow layer, so as to enhance the semantic expression on multiple scales. On the contrary, PAN transmits the positioning information from the shallow layer to the deep layer to enhance the positioning ability on multiple scales. The structure of FPN + PAN combines feature aggregation from different feature layers to improve the ability of the network to detect targets with different scales. FIGURE 5Open in figure viewerPowerPoint FPN+PAN structure In the Output, Yolov5 uses GIOU_ Loss as the loss function of the bounding box, as shown in Equations (1)–(3). C represents the minimum bounding box surrounding A and B. It can be seen from the equation that GIOU_ Loss not only pays attention to overlapping areas, but also other non-overlapping areas, which can better reflect the coincidence degree of the two and better carry out the regression of bounding box. At the same time, NMS operation is carried out to filter the multi-target box of target detection. I O U = A ∩ B A ∪ B \begin{equation}IOU = \frac{{A \cap B}}{{A \cup B}}\end{equation} (1) G I O U = I O U − C − A ∪ B ) C \begin{equation}GIOU=IOU-\frac{C-A\cup B)}{C}\end{equation} (2) G I O U _ L O S S = 1 − G I O U \begin{equation}GIOU\_LOSS = 1 - GIOU\end{equation} (3) 2.2 Modified Yolov5s architectures Yolov5s model has a good detection effect in the field of target detection, but it is implemented for general data sets such as COCO. It needs to be adjusted for the specific scene of gesture recognition, especially in the face of complex environment, different illumination and the influence of gesture occlusion on gesture recognition. The Yolov5s algorithm needs to be improved to meet the requirements of real-time recognition of hand occlusion on gesture. 2.2.1 Improvement of feature extraction Convolution neural network (CNN) convolves image to extract features through the convolution kernel. Convolution kernels move according to stride, and each convolution kernel extracts one feature of image. With the deepening of network layers, the number of convolution kernels and weights increases, and the speed of network extraction feature will slow down. In Ghost Module [8], Intrinsic Feature Maps are generated by convolution, and each channel in Intrinsic Feature Maps undergoes a linear transformation. The linear transformation is realized by Depthwise convolution [9] to obtain Ghost Feature Maps, and the Ghost Feature Maps and Intrinsic Feature Maps are concated to output the results. The process is shown in Figure 6. Compared with the traditional convolution, Ghost Module generates a large number of Ghost feature maps by linear convolution operation, which is to broaden the feature maps and generate more features. As a result, the number and weight of convolution kernels can be reduced correspondingly, and the detection speed will be accelerated. At the same time, Ghost feature map and Eigen feature map are spliced as output, and without deepening the depth of neural network, Eigen feature are reused to enhance the learning ability of network. FIGURE 6Open in figure viewerPowerPoint Ghost module convolution process GhostBottleneck [11] is composed of Ghost Module and has two structures, as shown in Figures 7 and 8. It can be seen from Figure 7 that when Stride = 1, the backbone path is composed of two Ghost Modules (GM) in series, in which the first GM increases the number of channels and the second GM reduces the number of channels to be consistent with the number of input channels. Add the Eigen feature map obtained in the first step and the Ghost feature map obtained in the second step to obtain the output of the final result to construct an efficient deep neural network. The difference between Figures 8 and 7 is that a Deepwise convolution with stride = 2 is added between two GMs in the backbone path, which reduces the size of the feature map to 1/2 of the input. Similarly, the jump connection path also needs the same down-sampling to ensure that the add operation can be aligned, and the final output image resolution is halved. FIGURE 7Open in figure viewerPowerPoint Stride = 1 bottleneck FIGURE 8Open in figure viewerPowerPoint Stride = 2 bottleneck In this paper, the BackBone3 × 3convolution layer is replaced by the GhostBottleneck module with a stride of two. The first one is used as an extension layer to increase the number of channels. The second one is to reduce the number of channels to match the shortcut path. Then, use shortcut to connect the input and output of these two Ghost Modules. GhostBottleneck module with a stride of 2 downsamples the image. Compared with traditional CNN, GhostBottleneck uses linear operations to obtain a huge number of similar feature maps, and enhances and augments the feature maps obtained by convolution. It increases the convolutional learning ability and extracts more features without deepening the network depth. The size of the output feature map is not changed at the same time, which reduces the total number of parameters and computational complexity, and reduces gesture training time. 2.2.2 Feature fusion and reuse Using dense connection network [10] for reference, the feature map after convolution extraction and the feature map before convolution extraction are concated together as the input of the next layer of feature extraction. The stitching of feature maps increases the number of feature maps in the lower resolution part of Yolov5s, and the feature map of each layer contains the output information of all previous layers. The stitching of feature maps can solve the problem of small target detection. Due to the reuse of features, the number of output features of feature maps is increased, the ability of network to extract features is strengthened, the detection effect is further improved, and the accuracy of target detection is improved. The modified Yolov5s network structure diagram is shown in the dotted line in Figure 9. The image is sliced by Focus, from 640 × 640 × 8 ${\rm{640}} \times {\rm{640}} \times {\rm{8}}$ pixel size to 320 × 320 × 32 ${\rm{320}} \times {\rm{320}} \times {\rm{32}}$ pixel size. After the GhostBottlecheck module with stride of two, the size of the feature map is 160 × 160 × 64 ${\rm{160}} \times {\rm{160}} \times {\rm{64}}$ . After that, the image features are extracted by the CSP module, and the size of the feature map remains unchanged. Concat the features extracted by the GhostBbottleneck module with the image features extracted by the CSP module to obtain a feature map with the size of 160 × 160 × 128 ${\rm{160}} \times {\rm{160}} \times 128$ . The feature map after splicing is extracted again by GhostBottlecheck module with stride of two again, and a feature map with a size of 80 × 80 × 128 $80 \times 80 \times 128$ is obtained. After feature extraction by CSP, the feature maps are spliced and sent to network for learning. The improved network adds two features concat, and the spliced features are sent to the next layer of network for feature learning. The number of layers is deeper and the learning ability is stronger. Compared with CNN convolution, GhostBottleneck module generates more features than ordinary convolution layer with fewer parameters, which greatly optimizes the model learning ability and makes gesture recognition rate higher. FIGURE 9Open in figure viewerPowerPoint Modified Yolov5s structure 3 MODEL TRAINING AND ADJUSTMENT 3.1 Gesture data collection The experimental data set samples mainly from six students in the same laboratory under different scenes, as shown in Figure 10, different lighting conditions to collect gesture data sets. Each gesture was rotated from different angles, and its position was transformed to increase the richness of the data set. The labelled box co-ordinates of gestures with the x-axis and y-axis values, and the width and height of that annotated image as shown in Figure 11, a total of 10,000 pictures of six gestures from zero to five were collected, with an average of 1667 for each gesture, and the ratio of training set to test set is 5:1. FIGURE 10Open in figure viewerPowerPoint Part of the Gesture dataset. (a) Gesture quantity histogram, (b) the labelled box coordinates of gestures with the x-axis and y-axis values, (c) the width and height of that annotated image FIGURE 11Open in figure viewerPowerPoint Dataset label information 3.2 Data labelling In the process of building gesture data set, the classification and bounding box information of the target is obtained by lambel software, and the information is saved in TXT format. The information includes the classification and the centre of the target ( x , y ) $( {x,y} )$ , the width of the bounding box w and the height of the bounding box h, the expression is as follows: x = x max + x min 2 a \begin{equation}x = \frac{{{x_{\max }} + {x_{\min }}}}{{2a}}\end{equation} (4) y = y max + y min 2 b \begin{equation}y = \frac{{{y_{\max }} + {y_{\min }}}}{{2b}}\end{equation} (5) w = x max − x min a \begin{equation}w = \frac{{{x_{\max }} - {x_{\min }}}}{a}\end{equation} (6) h = x max − y min b \begin{equation}h = \frac{{{x_{\max }} - {y_{\min }}}}{b}\end{equation} (7) Type in x max ${x_{\max }}$ and y max ${y_{\max }}$ represent the coordinates of the lower right corner of the bounding box; x min ${x_{\min }}$ and y min ${y_{\min }}$ represent the coordinates of the upper left corner of the bounding box;a,bthe width and height of the image. 4 ANALYSIS OF EXPERIMENTAL RESULTS 4.1 Experimental configuration This experiment uses Intel (R) core (TM) i5-6300hq CPU @ 2.30ghz processor, NVIDIA Geforce GTX 960 m independent graphics card, cuda11.0 version, cudnn8.0.5 version of the computer training, data set for 6 kinds of gestures. A total of 10,000 sheets, batch size 8, training 2000 rounds, the initial learning rate is 0.001, weight attenuation coefficient is 0.0005, and opencv is used for image display. 4.2 Model evaluation Mean average precision (mAP) was used as the evaluation standard. mAP is the average value of AP of all classifications in all pictures. AP is the average precision, which refers to the area under the PR curve of a specific class in all pictures. P is precision: accuracy, which refers to the ratio of the number of positive samples correctly predicted to the number of all predicted positive samples, and R is recall: recall rate, which refers to the ratio of the number of correctly predicted positive samples to the total number of real positive samples. The expressions of precision rate and recall rate are shown in Equations (8) and (9) Precision = T P T P + F P \begin{equation}{\rm{Precision}} = \frac{{TP}}{{TP + FP}}\end{equation} (8) Recall = T P T P + F N \begin{equation}{\rm{Recall}} = \frac{{TP}}{{TP + FN}}\end{equation} (9)where TP is true positive, the prediction is positive, and the actual prediction is positive, that is to say, the prediction of the algorithm is correct. FP is false positive, and the prediction is positive, but it is actually a negative example, that is, the algorithm is wrong in prediction. TN is true negative, and the predicted value is negative. In fact, it is a negative example, that is, the prediction of the algorithm is correct. FN is false negative, and the predicted value is negative. In fact, it is a positive example, that is, the algorithm is wrong in prediction. 4.3 Analysis of experimental results In the test set, there are 1667 gesture samples. Figure 12 shows the comparison of the modified Yolov5s model and the Yolov5s mAp value. According to the figure, the mAp value of the modified model is 94.49%, the mAp value of Yolov5s model is 91.4%, and the mAp value of the modified model is 3.38% higher than that of the unmodified model. The average accuracy of the improved yolov5s model is slightly higher than that of the unmodified Yolov5s model. Figure 13 shows the comparison of the recall rate curve of the modified Yolov5s model with that of the Yolov5s model. The recall rate of the modified Yolov5s model is 97.79%, and that of the Yolov5s model is 97.78%. The recall rate of the modified one and the unmodified one are the same, with little change. The prediction performance of the modified yolov5s model is better. FIGURE 12Open in figure viewerPowerPoint Comparison of mAP curve between modified Yolov5s and Yolov5s FIGURE 13Open in figure viewerPowerPoint Comparison of recall curve between modified Yolov5s and Yolov5s Figures 14-16 show the Classification_Loss curve of the modified Yolov5s model, GIOU_Loss curve, and Objectness_Loss curve. As can be seen from the figure, as the number of iteration increases, the loss of value is generally low, around 0.002, which proves that the training of the modified model is good. FIGURE 14Open in figure viewerPowerPoint Classification_Loss FIGURE 15Open in figure viewerPowerPoint GIOU_Loss FIGURE 16Open in figure viewerPowerPoint Objectness_Loss Figures 17 and 18 are the PR curves of the modified Yolov5s model and the Yolov5s model respectively. It can be seen from the figure that the AP value of the Yolov5s model is about 0.920, and the AP value of the modified Yolov5s model is about 0.942. The AP value of the modified Yolov5s model is 2.4% higher than that of the Yolov5s model, and the recognition accuracy is also improved. FIGURE 17Open in figure viewerPowerPoint Yolov5s AP curve FIGURE 18Open in figure viewerPowerPoint Modified Yolov5s AP curve In the experiment, we compared the modified Yolov5s,Yolov5s Yolov4, Yolov3, and SSD [12] algorithms. The comparison table of mAp value and FPS value is shown in Table 1. TABLE 1. mAP value and FPS value of each detection model and comparison table Detection model FPS (%) mAP (%) Precision (%) Recall (%) SSD 42 79.29 82.35 80.36 Yolov3 26 83.78 86.78 83.33 Yolov4 31 89.20 91.33 87.69 Yolov5s 49 91.4 92.38 89.79 Modified Yolov5s 48.8 94.49 95.81 92.61 It can be seen from Table 1 that compared with SSD model, Yolov3 model, Yolov4 model and Yolov5s model, the mAp value of the modified Yolov5s model is increased by 19.17%, 12.78%, 5.93% and 3.38% respectively; FPS is improved by 6.8, 22.8, 17.8 frames/s, respectively. the precision value of the modified Yolov5s model is increased by 16.3%, 10.4%, 4.9%, 3.71%. Compared with Yolov5s, the detection speed difference is small. Because the modified model has a higher image feature reuse rate, the prediction accuracy is improved. Adding image stitching in the backbone has a slight impact on the detection speed, which can be ignored. The model detection effect is shown in Figure 19. FIGURE 19Open in figure viewerPowerPoint Experimental detection effect of six kinds of gestures (a) Gesture 0 (b) Gesture 1 (c) Gesture 2 (d) Gesture 3 (e) Gesture 4 (f) Gesture 5 From the actual detection results, we can see that the improved model can accurately detect six different gestures in complex background. In the skin colour area, the modified network can effectively extract the hand features, distinguish the face from the hand, and make the gesture recognition result more accurate. In the dark light area, the modified network can accurately capture and recognize gestures, which improves the accuracy of gesture recognition under dark light conditions. Gesture recognition in different people and different environments is shown in Figure 20. It can be seen from the figure that the modified network has a good detection too. To sum up, the detection accuracy of the modified model is improved compared with that of the unmodified model. The mAp value and AP value are increased by 3.38% and 2.4% respectively, we get the Precision of 95.81%. and the real-time detection speed is 48.8 frames / s, which can fully meets the real-time performance of gesture recognition detection. FIGURE 20Open in figure viewerPowerPoint Experimental detection effect in different environment (a) Skin colour area (b) dark light area (c) strong light (d) complex environment (e) fuzzy environment (f) normal environment 5 CONCLUSION In order to solve the problem of high gesture similarity and low recognition rate in the field of human–computer cooperation, a modified Yolov5s gesture recognition method is proposed. Firstly, the convolutional neural network in the backbone of Yolov5s is replaced by the GhostBottlenkek module. By adding feature map pairs, namely, the ghost pair, to increase the number of feature maps and enhance the ability of model learning, so as to solve the problem of low recognition rate of target occlusion. At the same time, drawing on the idea of Densenet, the first four layers of the network GhostBottlecheck module and CSP module feature maps are concated respectively, and then transferred to the next layer network learning. Feature reuse is used to strengthen the learning ability of the network and promote the fusion of features. The results show that the modified model has high detection accuracy, the mAp value is increased by 3.38%, the AP value is increased by 2.4%, we get the Precision of 95.81%,the detection speed is 48.8 frames/s, which basically meets the real-time requirements of human–computer interaction. However, tensor stitching slightly slows down the detection speed. In the next work, we will continue to improve the accuracy and speed and study Chinese sign language gesture recognition. FUNDING INFORMATION This work was supported by National Natural Science Foundation of China (61903006), Beijing Natural Science Foundation (4204096). 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