1. Overview of algorithm theory

Fast-RCNN is a deep learning-based target detection algorithm that can be used to detect target objects in images. Traffic sign detection is an important task in traffic scenarios, which can help when traffic signs on the road are obscured or damaged. The traffic sign detection algorithm based on the Fast-RCNN deep learning network can detect images in traffic scenes, so as to realize the automatic detection and recognition of traffic signs. This algorithm can be applied in fields such as autonomous driving and traffic management.

The implementation steps of the algorithm are as follows:

Step 1. Dataset preparation

Prepare a dataset of traffic signs, including images of signs and corresponding labels. The label includes the category and location information of the sign.

Step 2. Feature Extraction

Feature extraction of traffic sign images using deep learning networks. In this algorithm, a pre-trained convolutional neural network can be used to extract the features of the image.

Step 3. Candidate box generation

A candidate box generation algorithm is used to generate multiple candidate boxes that may contain traffic signs in the image.

Step 4. Regional Proposal Network

A region proposal network is used to screen candidate boxes to obtain regions that may contain traffic signs. In this algorithm, the Selective Search algorithm can be used for region suggestion.

Step 5. Target Classification

Use the deep learning network to classify the candidate frame and judge whether the candidate frame contains traffic signs. In this algorithm, the Fast-RCNN network can be used for target classification.

Step 6. Targeting

Use the deep learning network to locate the candidate frame containing the traffic sign, and obtain the precise location of the traffic sign. In this algorithm, the Fast-RCNN network can be used for target positioning.

Mathematical formula
In the Fast-RCNN network, the specific expression of the mathematical formula is as follows:

Region proposal network:
$R = {r_{1}, r_{2}, ..., r_{k}}$

Among them, $R$ is the set of candidate boxes, and $r_{i}$ is the $i$th candidate box.

Target classification:
$p_{i} = softmax(W^{T}{c} \phi(r{i})+b_{c})$

Among them, $p_{i}$ is the predicted probability of the $i$th candidate box, $W_{c}$ is the weight of the classifier, $\phi(r_{i})$ is the feature vector of the candidate box, $ b_{c}$ is the bias of the classifier.

Target positioning:
$t_{i}^{} = (t_{x}^{}, t_{y}^{}, t_{w}^{}, t_{h}^{*})$

Among them, $t_{i}^{}$ is the real position of the $i$th candidate box, $t_{x}^{}$, $t_{y}^{}$, $t_{w}^{ }$ and $t_{h}^{*}$ are the $x$ coordinates, $y$ coordinates, width and height of the real position, respectively.

The application scenarios of this algorithm include autonomous driving, traffic management and other fields. In the field of automatic driving, this algorithm can be used for automatic identification of vehicles and planning of driving routes; in the field of traffic management, this algorithm can be used for automatic detection and recognition of traffic signs to improve the efficiency and accuracy of traffic management.

The advantages of this algorithm include:

High precision: The algorithm uses a deep learning network for target detection and has high detection accuracy.

Efficiency: The algorithm can efficiently process a large number of candidate boxes to achieve fast object detection.

Scalability: The algorithm can adapt to different application scenarios by adjusting the structure and parameters of the neural network.

Disadvantages of this algorithm include:

Large data requirements: The algorithm requires a large amount of labeled data to train the deep learning network.

High consumption of computing resources: This algorithm requires a large amount of computing and requires high computing resources.

High false detection rate: This algorithm is prone to false detections in traffic scenarios, and further optimization of the algorithm is required to reduce the false detection rate.

The traffic sign detection algorithm based on Fast-RCNN deep learning network is an efficient and accurate target detection algorithm, which can be used for traffic sign detection and recognition in traffic scenes. The algorithm realizes target detection through the steps of feature extraction, candidate box generation, region proposal network, target classification and target localization. The application scenarios of this algorithm include autonomous driving, traffic management and other fields, and it has the advantages of high precision, high efficiency and scalability. However, this algorithm requires a large amount of labeled data and computing resources, and the false detection rate is high. Further optimization of the algorithm is required to improve the detection accuracy and reduce the false detection rate.

2. Some core programs

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load initial_fastRCNN.mat% 加载预先训练好的Fast R-CNN模型初始权重
load Gtruth.mat% 加载训练图像的真实标签
% 将训练图像的路径与文件名拼接成完整路径
Signs.imageFilename = fullfile('train_image', Signs.imageFilename);

rng(0);% 设置随机数种子
% 随机打乱训练集图像的顺序
Idxs  = randperm(height(Signs));
% 根据打乱后的顺序重新排列训练集图像
Signs = Signs(Idxs,:);
% 创建ImageDatastore对象，用于存储训练图像
imds  = imageDatastore(Signs.imageFilename);
% 创建BoxLabelDatastore对象，用于存储训练集图像中目标的真实边界框标注
blds  = boxLabelDatastore(Signs(:,2:end));
% 将ImageDatastore对象和BoxLabelDatastore对象合并成一个数据集
ds    = combine(imds, blds);
% 对数据集进行预处理，将图像和边界框调整到指定大小
ds    = transform(ds,@(data)preprocessData(data,[920 968 3]));
% 设置训练选项，包括使用的优化算法、批量大小、学习率、最大训练轮数以及中间临时保存模型的路径

options = trainingOptions('sgdm', ...
    'MiniBatchSize', 10, ...
    'InitialLearnRate', 1e-3, ...
    'MaxEpochs', 10, ...
    'CheckpointPath', tempdir);
% 利用训练集对Fast R-CNN模型进行训练，并返回训练好的模型


frcnn = trainFastRCNNObjectDetector(ds, fastRCNNLayers , options, ...
    'NegativeOverlapRange', [0 0.1], ...
    'PositiveOverlapRange', [0.7 1]);

save FastRCNN.mat frcnn% 将训练好的Fast R-CNN模型保存到文件中。
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