Table of contents
1. Preview of algorithm operation renderings
2.Algorithm running software version
4. Overview of algorithm theory
5. Algorithm complete program engineering
1. Preview of algorithm operation renderings
2.Algorithm running software version
matlab2022a
3. Some core programs
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load FRCNN.mat
In_layer_Size = [224 224 3];
imgPath = 'train/'; % 图像库路径
imgDir = dir([imgPath '*.jpg']); % 遍历所有jpg格式文件
cnt = 0;
for i = 1:length(imgDir) % 遍历结构体就可以一一处理图片了
i
if mod(i,9)==1
figure
end
cnt = cnt+1;
subplot(3,3,cnt);
img = imread([imgPath imgDir(i).name]); %读取每张图片
I = imresize(img,In_layer_Size(1:2));
[bboxes,scores] = detect(detector,I);
[Vs,Is] = max(scores);
if isempty(bboxes)==0
I1 = insertObjectAnnotation(I,'rectangle',bboxes(Is,:),Vs);
else
I1 = I;
Vs = 0;
end
imshow(I1)
title(['检测置信度:',num2str(Vs)]);
if cnt==9
cnt=0;
end
end
In_layer_Size = [224 224 3];
imgPath = 'test/'; % 图像库路径
imgDir = dir([imgPath '*.jpg']); % 遍历所有jpg格式文件
cnt = 0;
for i = 1:length(imgDir) % 遍历结构体就可以一一处理图片了
i
if mod(i,5)==1
figure
end
cnt = cnt+1;
subplot(1,5,cnt);
img = imread([imgPath imgDir(i).name]); %读取每张图片
I = imresize(img,In_layer_Size(1:2));
[bboxes,scores] = detect(detector,I);
[Vs,Is] = max(scores);
if isempty(bboxes)==0
I1 = insertObjectAnnotation(I,'rectangle',bboxes(Is,:),Vs);
else
I1 = I;
Vs = 0;
end
imshow(I1)
title(['检测置信度:',num2str(Vs)]);
if cnt==5
cnt=0;
end
end
00574. Overview of algorithm theory
Fire detection is an important task in many fields, including construction, forests, and even space. In recent years, the application of deep learning networks to image recognition and classification has made significant progress, making fire detection algorithms based on deep learning increasingly common. Below, we will introduce in detail a fire detection algorithm based on convolutional neural network (CNN). Convolutional neural network (CNN) is a type of deep learning network that is particularly suitable for processing image data. CNN extracts and identifies features of images through a series of convolutional layers, pooling layers and fully connected layers. In fire detection, CNN can learn and identify the characteristics of fire from images to perform accurate fire detection.
Specifically, CNN's fire detection algorithm usually contains the following steps:
- Data preprocessing: Preprocess image data, such as size adjustment, normalization, etc., to facilitate neural network processing.
- Feature extraction: Extracting low-level to high-level features from the image through the first few layers of CNN (usually convolutional layers and pooling layers).
- Fire identification: Fire identification is performed based on the extracted features through the latter layers of CNN (usually the fully connected layer and output layer).
The mathematical formulas of CNN mainly involve convolution, pooling and activation functions.
- Convolution: Xi=f(Wi*X+bi), where Xi is the result of convolution, Wi is the convolution kernel, X is the input image, bi is the bias, and f is the activation function.
- Pooling: Generally, maximum pooling or average pooling is used to map a part of the input image to a single value.
- Activation function: such as ReLU (Rectified Linear Unit), etc., used to introduce nonlinearity and enhance the expression ability of the neural network.
Algorithm process
- Data preparation: Collect a large amount of fire and non-fire image data, label the images, and divide the data into training set, verification set and test set.
- Model construction: Build a CNN model, including multiple convolutional layers, pooling layers, fully connected layers, etc.
- Model training: Use the training set to train the model, and adjust the parameters of the model through the backpropagation algorithm to minimize prediction errors.
- Model verification: Use the verification set to verify the trained model and adjust the parameters of the model to obtain better performance.
- Model testing: Use the test set to evaluate the performance of the model and calculate the accuracy, recall, F1 score and other indicators of the model.
- Model application: Apply the trained model to actual fire detection tasks. It can be integrated into a monitoring system, or used to analyze images captured by satellites or drones, etc.
Advantages and Disadvantages
Fire detection algorithms based on deep learning have the following advantages:
- It can automatically learn and identify fire characteristics, greatly improving the accuracy and efficiency of fire detection.
- It can handle complex scenes and environments, such as night, thick smoke, occlusion, etc.
- It can handle multi-angle and multi-view image data.
But there are also some disadvantages:
- A large amount of annotated data is required for training and validation.
- The requirements for hardware equipment are relatively high, requiring high-performance GPU or TPU for calculation.
- Performance in some special scenarios (such as extreme cold, extreme heat, etc.) may be affected.
In general, fire detection algorithms based on deep learning have been widely used in many fields and have shown excellent performance. With the continuous development of deep learning technology, it is believed that this algorithm will be further optimized and improved in the future.
5. Algorithm complete program engineering
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