The German Traffic Sign Detection Benchmark


The German Traffic Sign Detection Benchmark is a single-image detection assessment for researchers with interest in the field of computer vision, pattern recognition and image-based driver assistance. It is introduced on the IEEE International Joint Conference on Neural Networks 2013. It features ...




Feel free to download the training data set (1.1 GB, GT only 25 kB) and use it for your purposes. We would like to encourage everyone developing a detector based on this data to also participate in the upcoming competition at the beginning of February 2013. The training download package comprises

Although the competition is limited to the aforementioned traffic sign categories (prohibitive, danger, mandatory) the annotations will encompass other traffic sign categories. We plan on providing a final package containing annotations for both training and evaluation dataset once the competition is over.


Image format


Annotation format

Annotations are provided in CSV files. Fields are seperated by a semicolon (;). They contain the following information:

You can download source code that will help you to read and compare the annotation files with your detector's results. For an explanation of the class IDs we refer to the ReadMe.txt in the download package.


Submission format and regulations

Similar to the annotation format, the result files that can be submitted during the competition phase (schedule) are supposed to be provided in a CSV file format, seperated by a semicolon (;). The fields are

Hence, the result file will contain one line per detection. You can download and use source code with functions to write those result files (C++, Matlab).

In the submission section you will be asked to choose a category (prohibitive, danger, mandatory) for your detection results. Please note that any detection of a sign that is not in that category will be counted as a false detection. For submitting the results of your detector you will have to upload a zip file. The zip file should contain the result text files of several runs with different parametrizations. The results are then evaluated and a precision-recall plot is created thereof. The precision-recall plot is always computed with all files you submitted so far.

Code snippets




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