Traffic sign detection method and device based on clustering analysis

Abstract

The invention provides a traffic sign detection method and device based on clustering analysis, and belongs to the technical field of traffic sign detection. The method comprises the following steps: preprocessing a multi-disturbance traffic sign data set, and determining a first centroid of the data set; calculating a non-connectable data set based on the data set, and sorting the first centroids to obtain a second centroid; and updating the data set by using the non-connectable data set to obtain a first data set, pulling data points of the first data set to a second centroid, performing updating, aggregation and separation processing on the data after data translation based on the first data set to obtain a second data set, and performing data clustering to obtain a data clustering result. The method comprises the following steps: acquiring an optimal unsupervised contour coefficient, marking unmarked original data on the basis of a clustering result of the optimal unsupervised contour coefficient, obtaining a marked multi-disturbance traffic sign data set, carrying out model training, identifying a to-be-identified traffic sign by adopting a trained traffic sign detection model, and determining the type of the traffic sign. According to the invention, data interference in a traffic sign identification process can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic sign detection, and in particular to a traffic sign detection method and device based on cluster analysis. Background Art

[0002] Clustering Analysis (CA) is an important research direction in the field of machine learning technology, which aims to divide data sets into different categories to reflect the attribution of samples. This simple and effective process plays an important role in many fields such as bioinformatics, information theory, and pattern recognition. However, the data overlap problem in the data set often has an adverse effect on the performance of clustering analysis technology, which is also one of the most complex and challenging tasks in the field of clustering analysis. The reason for this problem is that most data sets contain a certain number of special samples that belong to different categories but have very similar data features, so that the clustering analysis model can hardly distinguish the differences between them, and thus cannot correctly cluster the data.

[0003] In recent years, a lot of work has been done to solve the problem of data overlap. Most methods can be summarized into three categories: preset density-based clustering algorithms, dimensionality reduction techniques, and feature selection. Preset density-based clustering algorithms (such as DBSCAN and OPTICS) improve data overlap by identifying high-density areas, but these algorithms are sensitive to parameter selection and may not work well when dealing with non-uniform density data. Dimensionality reduction techniques (such as PCA and t-SNE) can effectively reduce data overlap caused by irrelevant features, but may lose key information useful for cluster analysis. Feature selection can select features that are more informative for cluster analysis, but this requires sufficient domain knowledge and computing resources, and sometimes it is difficult to find the exact combination of features to distinguish overlapping data.

[0004] Adversarial attacks in machine learning refer to the act of tampering with input data to cause the model to make incorrect predictions. Adversarial attacks usually generate images with only minor perturbations, resulting in overlap between clean and tampered images, which makes it possible for unsupervised pattern recognition models to fail to detect these differences, especially when the input data is subjected to unknown adversarial attacks. Such attacks pose a serious threat to transportation applications, such as causing errors in traffic sign recognition, incorrect vehicle route planning, and even rendering autonomous driving navigation systems ineffective. These threats can cause traffic accidents, endanger life safety, and cause significant economic losses and damage to infrastructure.

[0005] Therefore, it is necessary to construct and design a traffic sign detection and recognition method that can accurately identify traffic signs in order to reduce the difficulty of detection and improve the accuracy of detection. Summary of the invention

[0006] In view of this, an embodiment of the present invention provides a traffic sign detection method and device based on cluster analysis, so as to solve the problem that the existing traffic sign detection technology has great difficulty in detecting unknown adversarial attacks, resulting in inaccurate sign detection results.

[0007] The technical solution adopted by the present invention is: In a first aspect, the present invention provides a traffic sign detection method based on cluster analysis, comprising: S1. Obtain a multi-disturbance traffic sign dataset, and pre-process the multi-disturbance traffic sign dataset to obtain a numerical dataset; S2, performing data translation on the numerical data set, and performing cluster analysis on the data set after the data translation to determine the first centroid of the data set; S3. Based on the numerical data set, the unconnectable data set is calculated by using the Euclidean distance calculation function, and the first centroids are sorted to obtain the second centroids; S4, using the unconnectable data set to update the data set after the data translation to obtain a first data set, and pulling the data points of the first data set to the second centroid, updating the data after the data translation based on the first data set, and aggregating and separating the data points of the first data set to obtain a second data set; S5. Clustering the second data set using a preset clustering algorithm, and calculating an unsupervised silhouette coefficient of the clustering result; S6, iteratively executing steps S3-S5, determining an optimal unsupervised contour coefficient from multiple unsupervised contour coefficients, and determining typical sizes and characteristic distributions of various types of traffic signs based on clustering results of the multi-disturbance traffic sign data set corresponding to the optimal unsupervised contour coefficient, and annotating the unannotated original data to obtain an annotated multi-disturbance traffic sign data set; S7. Train a preset traffic sign detection model based on the labeled multi-disturbance traffic sign dataset, and use the trained traffic sign detection model to identify the traffic sign to be identified, and confirm the category of the traffic sign to be identified.

[0008] Furthermore, in S1, a multi-disturbance traffic sign dataset is obtained, and the multi-disturbance traffic sign dataset is preprocessed to obtain a numerical dataset, including: S11: Obtaining a multi-perturbation traffic sign dataset , define the following image data expansion formula: in, Represents the height of the image, Represents the width of the image, represents the number of color channels RGB, flatten represents the flatten function; using the above expansion formula, the data set Each three-dimensional image data in is expanded into one-dimensional numerical data in turn. ; S12: Combine the expanded one-dimensional numerical data into a new numerical data set , which contains samples, each sample represents an image and contains Features.

[0009] Furthermore, in S2, data translation is performed on the numerical data set, and cluster analysis is performed on the data set after the data translation to determine the first centroid of the data set, including: S21: According to the formula , translate the numerical data set in the positive direction as a whole, so that all the eigenvalues ​​in the data set are non-negative, and obtain the data set after data translation β ; S22: Using a preset clustering algorithm from the data set β Calculate k centroids and record the index values ​​of k centroids .

[0010] Furthermore, in S3, based on the numerical data set, the unconnectable data set is calculated by the Euclidean distance calculation function, and the first centroids are sorted to obtain the second centroids, including: S31: Define the following Euclidean distance calculation function: in, It is a data point and The Euclidean distance between them, m is the data set The number of features of the data points in ; S32: Calculate the data set using the Euclidean distance calculation function Each data point and reference point The Euclidean distance of Reorder the data points in to obtain a non-connectable data set The reference point Used to calculate Euclidean distance to generate unconnectable datasets ψ , α is the minimum centroid of the sum of the eigenvalues ; S33: According to the formula , reorder the k centroids to get the second centroid ; Among them, argsort represents the argsort function, represents the centroid with the smallest sum of eigenvalues; distance Represents the Euclidean distance calculation function; I Represents the index value after centroid sorting, Represents the second centroid after reordering.

[0011] Furthermore, in S4, the unconnectable data set is used to update the data set after the data translation to obtain a first data set, and the data points of the first data set are pulled to the second centroid, the data after the data translation is updated based on the first data set, and the data points of the first data set are aggregated and separated to obtain a second data set, including: S41: Dataset Store to a temporary dataset middle; S42: Based on unconnectable datasets , according to the formula , update the dataset , get the first data set, and pull all data points in the first data set to the second centroid ;in, Representation dataset The i data points; Representation dataset The i data points; Represents the second centroid In the dataset The corresponding data points in ; Representation dataset The feature mean of all data points in ; S43: For each second centroid , execute step S42 once, and repeat k times in total; S44: According to the formula , update the dataset , separate data points of different categories, aggregate data points of the same category, and obtain the second data set; Representation dataset The i data points.

[0012] Furthermore, clustering the second data set using a preset clustering algorithm and calculating an unsupervised silhouette coefficient of the clustering result includes: Use the preset clustering algorithm to cluster the data points of the second data set to obtain the clustering results ; Based on the data set And clustering results , calculate the unsupervised silhouette coefficient of this clustering.

[0013] In a second aspect, the present invention provides a traffic sign detection device based on cluster analysis, comprising: A data set preprocessing module is used to obtain a multi-disturbance traffic sign data set, preprocess the multi-disturbance traffic sign data set, and obtain a numerical data set; The data translation analysis module is used to translate the numerical data set, and perform cluster analysis on the data set after the data translation to determine the first centroid of the data set; The centroid sorting module is used to calculate the unconnectable data set based on the numerical data set through the Euclidean distance calculation function, and sort the first centroid to obtain the second centroid; A data set updating module, used to update the data set after data translation by using the unconnectable data set to obtain a first data set, and pull the data points of the first data set to the second centroid, update the data after data translation based on the first data set, and aggregate and separate the data points of the first data set to obtain a second data set; A data clustering processing module, used to cluster the second data set using a preset clustering algorithm and calculate an unsupervised silhouette coefficient of the clustering result; A sign clustering analysis module is used to determine the optimal unsupervised silhouette coefficient from multiple unsupervised silhouette coefficients, and determine the typical size and characteristic distribution of various types of traffic signs based on the clustering results of the multi-disturbance traffic sign data set corresponding to the optimal unsupervised silhouette coefficient, and annotate the unannotated original data to obtain the annotated multi-disturbance traffic sign data set; The sign detection module is used to train a preset traffic sign detection model based on the labeled multi-disturbance traffic sign data set, and use the trained traffic sign detection model to identify the traffic sign to be identified, and confirm the category of the traffic sign to be identified.

[0014] In summary, the beneficial effects of the present invention are as follows: The present invention provides a traffic sign detection method based on cluster analysis. In the process of processing a multi-disturbance traffic sign data set, the method introduces an unconnectable data set based on distance calculation, and changes the distribution of the data through repeated aggregation and separation processing, thereby obtaining the optimal unsupervised contour coefficient for accurately identifying the size of various types of traffic signs, solving the data overlap problem during cluster analysis of traffic sign data, and making it difficult to detect unknown adversarial attacks, resulting in inaccurate sign detection results. At the same time, the present invention also trains a preset traffic sign detection model based on the size of various types of traffic signs, uses the trained traffic sign detection model to identify the traffic sign to be identified, and confirms the category of the traffic sign to be identified, so that accurate detection and identification of traffic signs can be achieved when there is interference in the original traffic sign data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the following is a brief introduction to the drawings required for use in the embodiment of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work, and these are all within the protection scope of the present invention.

[0016] Figure 1 A flow chart of a traffic sign detection method based on cluster analysis of the present invention; Figure 2 Schematic diagram of the data iterative processing process in the present invention; Figure 3 This is a functional module diagram of a traffic sign detection device based on cluster analysis in the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. If there is no conflict, the various features of the present invention and the embodiments can be combined with each other, all within the scope of protection of the present invention.

[0018] Embodiment 1: See also Figure 1 , Figure 1 FIG. 1 is a flow chart of a traffic sign detection method based on cluster analysis in Embodiment 1 of the present invention. Figure 1 As shown, the method provided by the present invention comprises: S1. Obtain a multi-disturbance traffic sign dataset, and pre-process the multi-disturbance traffic sign dataset to obtain a numerical dataset; S2, performing data translation on the numerical data set, and performing cluster analysis on the data set after the data translation to determine the first centroid of the data set; S3. Based on the numerical data set, the unconnectable data set is calculated by using the Euclidean distance calculation function, and the first centroids are sorted to obtain the second centroids; S4, using the unconnectable data set to update the data set after the data translation to obtain a first data set, and pulling the data points of the first data set to the second centroid, updating the data after the data translation based on the first data set, and aggregating and separating the data points of the first data set to obtain a second data set; S5. Clustering the second data set using a preset clustering algorithm, and calculating an unsupervised silhouette coefficient of the clustering result; S6, iteratively executing steps S3-S5, determining an optimal unsupervised contour coefficient from multiple unsupervised contour coefficients, and determining typical sizes and characteristic distributions of various types of traffic signs based on clustering results of the multi-disturbance traffic sign data set corresponding to the optimal unsupervised contour coefficient, and annotating the unannotated original data to obtain an annotated multi-disturbance traffic sign data set; S7. Train a preset traffic sign detection model based on the labeled multi-disturbance traffic sign dataset, and use the trained traffic sign detection model to identify the traffic sign to be identified, and confirm the category of the traffic sign to be identified.

[0019] Further, in the embodiment of the present invention, obtaining a multi-disturbance traffic sign dataset in S1, preprocessing the multi-disturbance traffic sign dataset to obtain a numerical dataset specifically includes the following sub-steps: S11: Obtaining a multi-perturbation traffic sign dataset , define the following image data expansion formula: in, Represents the height of the image, Represents the width of the image, represents the number of color channels RGB, flatten represents the flatten function; using the above expansion formula, the data set Each three-dimensional image data in is expanded into one-dimensional numerical data in turn. ; S12: Combine the expanded one-dimensional numerical data into a new numerical data set , which contains samples, each sample represents an image and contains Features.

[0020] The embodiment of the present invention converts three-dimensional image data into one-dimensional numerical data for processing, which can facilitate the subsequent data processing process to quickly complete data clustering and analysis, thereby improving the data processing speed.

[0021] Further, in the embodiment of the present invention, in S2, data translation is performed on the numerical data set, and cluster analysis is performed on the data set after the data translation to determine the first centroid of the data set, which specifically includes the following sub-steps: S21: According to the formula , translate the numerical data set in the positive direction as a whole, so that all the eigenvalues ​​in the data set are non-negative, and obtain the data set after data translation β。

[0022] S22: Using a preset clustering algorithm from the data set β Calculate k centroids and record the index values ​​of k centroids In addition, you can also β Manually select k centroids in .

[0023] The preset clustering algorithm may be a k-means clustering algorithm, that is, a K-means clustering algorithm.

[0024] Further, in the embodiment of the present invention, in S3, based on the numerical data set, the unconnectable data set is calculated by the Euclidean distance calculation function, and the first centroids are sorted to obtain the second centroids, including but not limited to the following sub-steps: S31: Define the following Euclidean distance calculation function: in, It is a data point and The Euclidean distance between them, m is the data set The number of features of the data points in ; S32: Calculate the data set using the Euclidean distance calculation function Each data point and reference point The Euclidean distance of Reorder the data points in to obtain a non-connectable data set The reference point Used to calculate Euclidean distance to generate unconnectable datasets ψ , α is the minimum centroid of the sum of the eigenvalues ; S33: According to the formula , reorder the k centroids to get the second centroid ; Among them, argsort represents the argsort function, represents the centroid with the smallest sum of eigenvalues; distance Represents the Euclidean distance calculation function; I Represents the index value after centroid sorting, Represents the second centroid after reordering.

[0025] Further, in the embodiment of the present invention, in S4, the unconnectable data set is used to update the data set after the data translation to obtain the first data set, and the data points of the first data set are pulled to the second centroid, the data after the data translation is updated based on the first data set, and the data points of the first data set are aggregated and separated to obtain the second data set, including: S41: Build a temporary data set and convert the data set Store to a temporary dataset middle.

[0026] S42: Based on unconnectable datasets , according to the formula , update the dataset , get the first data set, and pull all data points in the first data set to the second centroid ;in, Representation dataset The i data points; Representation dataset The i data points; Represents the second centroid In the dataset The corresponding data points in ; Representation dataset The feature mean of all data points in .

[0027] S43: For each second centroid , execute step S42 once, and repeat k times in total. The iterative processing of data is as follows: Figure 2 shown.

[0028] S44: According to the formula , update the dataset , separate data points of different categories, aggregate data points of the same category, and obtain the second data set; Representation dataset The i data points.

[0029] Furthermore, in the embodiment of the present invention, clustering the second data set using a preset clustering algorithm and calculating an unsupervised silhouette coefficient of the clustering result in S5 specifically includes the following sub-steps: S51: clustering the data points of the second data set using a preset clustering algorithm to obtain a clustering result ; S52: Based on the data set And clustering results , calculate the unsupervised silhouette coefficient of this clustering.

[0030] Specifically, in the embodiment of the present invention, multiple unsupervised contour coefficients are obtained by iteratively clustering the data set. At this time, the optimal unsupervised contour coefficient is obtained from the multiple unsupervised contour coefficients by performing coefficient optimization analysis on the unsupervised contour coefficients, and the typical size (such as aspect ratio, pixel range) and feature distribution (such as color, cluster center) of each type of traffic sign are determined based on the clustering result of the multi-disturbance traffic sign data set corresponding to the optimal unsupervised contour coefficient. At the same time, the unlabeled original data is labeled (i.e., pseudo labels are added) to obtain the labeled multi-disturbance traffic sign data set. If the original data has been labeled, the noise label can be corrected in combination with the clustering result.

[0031] Finally, the present invention trains a preset traffic sign detection model based on the labeled multi-disturbance traffic sign data set, and uses the trained traffic sign detection model to identify the traffic sign to be identified, thereby confirming the category of the traffic sign to be identified. Among them, the preset traffic sign detection model can be obtained by pre-training using the existing Yolov5 model.

[0032] Example 2: Reference Figure 3 As shown, the present invention provides a traffic sign detection device based on cluster analysis, comprising: A data set preprocessing module is used to obtain a multi-disturbance traffic sign data set, preprocess the multi-disturbance traffic sign data set, and obtain a numerical data set; The data translation analysis module is used to translate the numerical data set, and perform cluster analysis on the data set after the data translation to determine the first centroid of the data set; The centroid sorting module is used to calculate the unconnectable data set based on the numerical data set through the Euclidean distance calculation function, and sort the first centroid to obtain the second centroid; A data set updating module, used to update the data set after data translation by using the unconnectable data set to obtain a first data set, and pull the data points of the first data set to the second centroid, update the data after data translation based on the first data set, and aggregate and separate the data points of the first data set to obtain a second data set; A data clustering processing module, used to cluster the second data set using a preset clustering algorithm and calculate an unsupervised silhouette coefficient of the clustering result; A sign clustering analysis module is used to determine the optimal unsupervised silhouette coefficient from multiple unsupervised silhouette coefficients, and determine the typical size and characteristic distribution of various types of traffic signs based on the clustering results of the multi-disturbance traffic sign data set corresponding to the optimal unsupervised silhouette coefficient, and annotate the unannotated original data to obtain the annotated multi-disturbance traffic sign data set; The sign detection module is used to train a preset traffic sign detection model based on the labeled multi-disturbance traffic sign data set, and use the trained traffic sign detection model to identify the traffic sign to be identified, and confirm the category of the traffic sign to be identified.

[0033] Specifically, in the embodiment of the present invention, the device does not require feature selection and data dimension reduction operations, and solves the problem of data overlap. On the other hand, the device greatly improves the application scope of each clustering analysis process in the device through unlabeled data and unsupervised verification process, and reduces data interference in the data set.

[0034] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A traffic sign detection method based on cluster analysis, characterized in that: include: S1. Obtain a multi-disturbance traffic sign dataset, and pre-process the multi-disturbance traffic sign dataset to obtain a numerical dataset; S2, performing data translation on the numerical data set, and performing cluster analysis on the data set after the data translation to determine the first centroid of the data set; S3. Based on the numerical data set, the unconnectable data set is calculated by using the Euclidean distance calculation function, and the first centroids are sorted to obtain the second centroids; S4, using the unconnectable data set to update the data set after the data translation to obtain a first data set, and pulling the data points of the first data set to the second centroid, updating the data after the data translation based on the first data set, and aggregating and separating the data points of the first data set to obtain a second data set; S5. Clustering the second data set using a preset clustering algorithm, and calculating an unsupervised silhouette coefficient of the clustering result; S6, iteratively executing steps S3-S5, determining an optimal unsupervised contour coefficient from multiple unsupervised contour coefficients, and determining typical sizes and characteristic distributions of various types of traffic signs based on clustering results of the multi-disturbance traffic sign data set corresponding to the optimal unsupervised contour coefficient, and annotating the unannotated original data to obtain an annotated multi-disturbance traffic sign data set; S7. Train a preset traffic sign detection model based on the labeled multi-disturbance traffic sign dataset, and use the trained traffic sign detection model to identify the traffic sign to be identified, and confirm the category of the traffic sign to be identified.

2. The traffic sign detection method based on cluster analysis according to claim 1, characterized in that: In S1, a multi-disturbance traffic sign dataset is obtained, and the multi-disturbance traffic sign dataset is preprocessed to obtain a numerical dataset, including: S11: Obtaining a multi-perturbation traffic sign dataset , define the following image data expansion formula: in, Represents the height of the image, Represents the width of the image, represents the number of color channels RGB, flatten represents the flatten function; using the above expansion formula, the data set Each three-dimensional image data in is expanded into one-dimensional numerical data in turn. ; S12: Combine the expanded one-dimensional numerical data into a new numerical data set , which contains samples, each sample represents an image and contains Features.

3. The traffic sign detection method based on cluster analysis according to claim 1, characterized in that: In S2, data translation is performed on the numerical data set, and cluster analysis is performed on the data set after the data translation to determine the first centroid of the data set, including: S21: According to the formula , translate the numerical data set in the positive direction as a whole, so that all the eigenvalues ​​in the data set are non-negative, and obtain the data set after data translation β ; S22: Using a preset clustering algorithm from the data set β Calculate k centroids and record the index values ​​of k centroids .

4. The traffic sign detection method based on cluster analysis according to claim 1, characterized in that: In the S3, based on the numerical data set, the unconnectable data set is calculated by the Euclidean distance calculation function, and the first centroid is sorted to obtain the second centroid, including: S31: Define the Euclidean distance calculation function: in, It is a data point and The Euclidean distance between m It is a dataset The number of features of the data points in ; S32: Calculate the data set using the Euclidean distance calculation function Each data point and reference point The Euclidean distance of Reorder the data points in to obtain a non-connectable data set The reference point Used to calculate Euclidean distance to generate unconnectable datasets ψ , α is the minimum centroid of the sum of the eigenvalues ; S33: According to the formula , reorder the k centroids to get the second centroid ; Among them, argsort represents the argsort function, represents the centroid with the smallest sum of eigenvalues; distance Represents the Euclidean distance calculation function; I Represents the index value after centroid sorting, Represents the second centroid after reordering.

5. The traffic sign detection method based on cluster analysis according to claim 1, characterized in that: In S4, the unconnectable data set is used to update the data set after the data translation to obtain a first data set, and the data points of the first data set are pulled to the second centroid, the data after the data translation is updated based on the first data set, and the data points of the first data set are aggregated and separated to obtain a second data set, including: S41: Dataset Store in a temporary dataset middle; S42: Based on unconnectable datasets , according to the formula , update the dataset , get the first data set, and pull all data points in the first data set to the second centroid ;in, Representation dataset The i data points; Representation dataset The i data points; Represents the second centroid In the dataset The corresponding data points in ; Representation dataset The feature mean of all data points in ; S43: For each second centroid , execute step S42 once, and repeat k times in total; S44: According to the formula , update the dataset , separate data points of different categories, aggregate data points of the same category, and obtain the second data set; Representation dataset The i data points.

6. The traffic sign detection method based on cluster analysis according to claim 1, characterized in that: The method of clustering the second data set using a preset clustering algorithm and calculating an unsupervised silhouette coefficient of the clustering result includes: Use the preset clustering algorithm to cluster the data points of the second data set to obtain the clustering results ; Based on the data set And clustering results , calculate the unsupervised silhouette coefficient of this clustering.

7. A traffic sign detection device based on cluster analysis, characterized in that: include: A data set preprocessing module is used to obtain a multi-disturbance traffic sign data set, preprocess the multi-disturbance traffic sign data set, and obtain a numerical data set; The data translation analysis module is used to translate the numerical data set, and perform cluster analysis on the data set after the data translation to determine the first centroid of the data set; The centroid sorting module is used to calculate the unconnectable data set based on the numerical data set through the Euclidean distance calculation function, and sort the first centroid to obtain the second centroid; A data set updating module, used to update the data set after data translation by using the unconnectable data set to obtain a first data set, and pull the data points of the first data set to the second centroid, update the data after data translation based on the first data set, and aggregate and separate the data points of the first data set to obtain a second data set; A data clustering processing module, used to cluster the second data set using a preset clustering algorithm and calculate an unsupervised silhouette coefficient of the clustering result; A sign clustering analysis module is used to determine the optimal unsupervised silhouette coefficient from multiple unsupervised silhouette coefficients, and determine the typical size and characteristic distribution of various types of traffic signs based on the clustering results of the multi-disturbance traffic sign data set corresponding to the optimal unsupervised silhouette coefficient, and annotate the unannotated original data to obtain the annotated multi-disturbance traffic sign data set; The sign detection module is used to train a preset traffic sign detection model based on the labeled multi-disturbance traffic sign data set, and use the trained traffic sign detection model to identify the traffic sign to be identified, and confirm the category of the traffic sign to be identified.

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