Based on the YOLOv5 series [n/s/m/l] model development and construction of human gesture target detection, recognition and analysis system

Human gesture detection and recognition refers to the automatic recognition and understanding of human gestures through computer vision and deep learning technology. This technology can be applied in various fields, such as human-computer interaction, virtual reality, intelligent monitoring, etc.

The following is the general human gesture detection and recognition process:

1. Data collection: First, you need to collect training data including gestures. These data can capture images or video sequences of the human body through cameras or depth sensors.

2. Preprocessing: Preprocessing the collected images or videos, including image noise reduction, size adjustment, human pose estimation, etc. The goal is to extract clear and accurate images of the human body.

3. Hand key point detection: use key point detection algorithms, such as human body pose estimation models (such as OpenPose, HRNet, etc.), to detect and locate the hand area of ​​the human body in the image, and obtain the position information of the finger joints.

4. Feature extraction: Based on the position information of the key points of the hand, the feature representation of the gesture can be calculated. Common methods include using the distance between fingers, angles, directions, etc. to represent the shape and motion of gestures.

5. Build a classification model: Build a gesture classification model using machine learning or deep learning algorithms. Traditional machine learning algorithms such as support vector machines (SVM) can be used, as well as deep learning models such as convolutional neural networks (CNN) or recurrent neural networks (RNN).

6. Training and optimization: use the marked gesture data set for training, and optimize the model parameters through the back propagation algorithm to improve the accuracy and generalization ability of the model.

7. Gesture recognition: use the trained model to recognize new gesture images or video sequences. Input the image into the model to obtain the corresponding gesture category or label.

8. Application fields: According to application requirements, gesture actions can be applied to various scenarios, such as game control, gesture interaction interface, gesture voice recognition, etc.

Human gesture detection and recognition is a complex task that requires a combination of techniques such as image processing, computer vision, and machine learning. With the development of deep learning, modern gesture recognition systems have achieved remarkable progress in accuracy and real-time performance.

The core purpose of this article is to try to develop and build a human gesture detection and recognition model based on the YOLOv5 target detection model as an example. First, look at the effect examples:

 The data set is derived from manual data annotation, as follows:

 The training data configuration file looks like this:

# Dataset
path: ./dataset
train:
  - images/train
val:
  - images/test
test:
  - images/test

# Classes 
names:
  0: 0
  1: 1
  2: 2
  3: 3
  4: 4
  5: 5
  6: 6
  7: 7
  8: 8
  9: 9

YOLOv5 is an object detection algorithm based on deep learning, which is improved and optimized on the basis of YOLOv3. YOLOv5 provides models of different parameter magnitudes, called YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, respectively, where s, m, l, and x represent small, medium, large, and extra large.

The main differences between these models with different parameter magnitudes are the depth and width of the network, and the image size used in the training process. Specifically, the following is a brief description of each model:

• YOLOv5s: This is the smallest model with a smaller amount of parameters. It is suitable for resource-constrained devices or scenarios that require fast inference. YOLOv5s achieves a good balance between detection speed and accuracy.

• YOLOv5m: This is a medium-scale model between small and large models. It is the default model in the YOLOv5 series, which provides higher detection accuracy and is suitable for a variety of application scenarios.

• YOLOv5l: This is a larger model with more parameters than YOLOv5m. Compared with YOLOv5m, YOLOv5l has a slight improvement in accuracy, but requires more computing resources.

• YOLOv5x: This is the model with the most parameters in the YOLOv5 series, and it is also the largest model. It provides the highest detection accuracy but requires more computing resources. YOLOv5x is suitable for scenarios that require high precision, such as fine-grained target detection.

Overall, YOLOv5s is suitable for resource-constrained situations, YOLOv5m is a balanced choice, and YOLOv5l and YOLOv5x are suitable for tasks that require higher precision. Choosing a suitable model version depends on factors such as specific application scenarios, hardware resources, and performance requirements.

Here, models with different parameter levels of n, s, m, and l are also developed to compare performance differences. The default is 100 epoch iteration calculations, and the model training log output is as follows:

 Next, look at the specific result details.

【yolov5n】

 【yolov5s】

  【yolov5m】

【yolov5l】

 Next, we compared these four models of different magnitudes as a whole, as follows:

The first is the F1 value curve. The F1 value is a commonly used evaluation index to measure the comprehensive performance of the classification model. It combines precision (Precision) and recall (Recall) to provide a balanced evaluation of the model on positive and negative samples.

The calculation formula of F1 value is as follows: F1 = 2 * (Precision * Recall) / (Precision + Recall)

Among them, Precision represents the proportion of the predicted positive examples that are actually positive examples, which can be understood as the accuracy of the model. Recall indicates the proportion of the actual positive examples that are correctly predicted as positive examples, also known as the recall rate.

The value range of F1 value is between 0 and 1, and the closer the value is to 1, the better the classification performance of the model is. When Precision and Recall are high at the same time, the F1 value will be closer to 1; when there is a large difference between Precision and Recall, the F1 value will be lower.

The F1 value is suitable for binary classification problems where the two classes are balanced or unbalanced. For situations where the importance of different categories is inconsistent, weighted F1 values ​​can be used to adjust the calculation of F1 values ​​by assigning different weights to each category.

All in all, the F1 value is an indicator that comprehensively considers accuracy and recall, and is used to evaluate the overall performance of the classification model. It is one of the important indicators for judging the pros and cons of classifiers in different scenarios.

 The core code implementation is as follows:

def F1(P,R):
    """
    F1值
    """
    return 2*P*R/(P+R)

The result looks like this:

 Next is the loss curve, as follows:

 After that is the Precision precision rate and Recall recall rate curve.

Precision is used to evaluate the ability of the classification model to be a positive example in the samples predicted to be positive examples, also known as positive predictive value. It is one of the performance metrics for classification models.

The calculation formula of precision rate is as follows: Precision = TP / (TP + FP)

Among them, TP means True Positive (True Positive), that is, the number of samples that are correctly predicted as positive examples; FP means False Positive (False Positive), that is, the number of samples that are incorrectly predicted as positive examples.

The value range of precision rate is between 0 and 1, and the higher the value, the higher the accuracy of the model in predicting positive examples. A high precision means that the model is able to misclassify negative examples as positive examples as little as possible.

Accuracy is an important indicator when solving a specific problem. For example, in spam filtering, we are more concerned about misjudging normal emails as spam, so we hope to obtain a high accuracy rate. However, it should be noted that the precision rate can only provide an understanding of the accuracy of the model in the samples predicted as positive examples, and cannot reflect the predictive ability of the model for negative examples.

When evaluating the performance of a classification model, it is usually necessary to comprehensively consider other indicators such as recall rate, F1 value, etc. These metrics can provide a more comprehensive performance assessment and help us understand the performance of the model in different aspects.

The recall rate (Recall), also known as the recall rate, is used to evaluate the ability of a classification model to correctly predict as a positive example in all actual positive examples. It is one of the performance metrics for classification models.

The formula for calculating the recall rate is as follows: Recall = TP / (TP + FN)

Among them, TP means True Positive (True Positive), that is, the number of samples that are correctly predicted as positive examples; FN means False Negative (False Negative), that is, the number of samples that are incorrectly predicted as negative examples.

The value range of the recall rate is between 0 and 1, and the higher the value, the stronger the ability of the model in finding the actual positive examples. A high recall rate means that the model can minimize the number of cases where positive examples are missed as negative examples.

Recall is often used in tasks where finding all positive cases is critical, such as finding patients with a certain disease in medical diagnosis. In this case, we pay more attention to correctly predicting the actual positive examples, and can tolerate some misjudgments as positive examples.

However, it should be noted that recall alone cannot evaluate the performance of a classification model. In an imbalanced dataset, higher recall may result in lower precision. Therefore, when evaluating the performance of the classification model, it is necessary to comprehensively consider other indicators such as accuracy rate, F1 value, etc., to obtain a more comprehensive performance evaluation.

The precision rate curve looks like this:

 The recall curve looks like this:

 Later, when I have time, I will continue to develop the models of the yolox series for overall comparison.