1. Overview of algorithm theory

MATLAB simulation of traffic sign detection algorithm based on RCNN (Region-based Convolutional Neural Network) deep learning network. The algorithm uses a deep learning network for target detection, and according to the characteristics and challenges of traffic signs, the corresponding implementation steps are designed, and the difficulties in the implementation are analyzed. Through the research of this paper, the traffic sign detection algorithm based on deep learning can be further understood and applied.

Traffic sign detection plays an important role in intelligent transportation systems and driver assistance systems. Traditional feature engineering-based methods often need to manually extract features and design classifiers, and the effect is limited. While deep learning-based methods, such as RCNN, can automatically learn features and classifiers from data, with better performance and generalization ability.

1.1 Network training

Use a pretrained deep learning network, such as AlexNet or VGGNet, as a feature extractor.
The traffic sign dataset is fed into the network for training, and features and classifiers are learned simultaneously in an end-to-end manner.
Through the backpropagation algorithm and the gradient descent optimization algorithm, the weights and biases of the network are updated, so that the network can better predict the location and category of traffic signs.

1.2 Target detection

Preprocess the image to be detected, including image size adjustment, normalization, enhancement, etc., to improve detection performance and robustness.
Run the trained network on the preprocessed image to extract candidate regions and corresponding features.
Apply the non-maximum suppression (NMS) algorithm to the candidate area to remove highly overlapping candidate boxes.
Classifiers are used to classify each candidate area, and the candidate boxes are screened according to the classification result and confidence level to obtain the final traffic sign detection result.
The deep learning network model can be expressed as: Z = f(WX + b) where Z is the output of the network, W is the weight matrix, X is the input feature, b is the bias vector, and f is the activation function.

1.3 Object Detection Evaluation Index

Commonly used target detection evaluation indicators include accuracy rate, recall rate, F1 score, etc., which can be expressed by the following formula: Accuracy rate = number of correctly detected traffic signs / total number of detected traffic signs Recall rate = number of correctly detected traffic signs / true The number of traffic signs F1 score = 2 * (precision rate * recall rate) / (precision rate + recall rate)

The structure diagram of the complete R-CNN:

2. Some core programs

folder = 'test_images/';% 测试图像文件夹路径
file_list = dir(fullfile(folder, '*.jpg'));% 获取文件夹中所有jpg格式的图像文件列表

for i = 1:7% 对前7张图像进行目标检测和可视化
    img = imread(file_list(i).name);% 读取图像
    [bbox, score, label] = detect(frcnn, img);% 使用RCNN模型对图像进行目标检测
    if isempty(label)==0

        % 在图像上插入目标边界框和置信度
    detectedImg = insertObjectAnnotation(img,'rectangle',bbox,score);
    figure
    imshow(detectedImg) % 显示带有目标边界框和置信度的图像
    clear bbox score
    else% 如果未检测到目标
    figure
    imshow(img);title('检测失败');% 显示原始图像，并显示检测失败的标识
    clear bbox score
    end

end
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