In recent years, with the development of automatic drive, lane departure warning(LDW) and lane keeping system (LKS) are also in high demand, one of the bases of these two functions is lane detection. At present, there are many datasets of relevant lane line detection based on visible light scene, however, visible light faces many problems in practical application, for example, it cannot be accurately identified in strong light or at night. Infrared itself has a good adaptability to these scenes, but there are few datasets of lane line detection based on infrared in real scenes. To solve this problem, we create a new database of vehicle-mounted scenes, which contains lane line data under infrared. The dataset contains lane line data under infrared, which can be used to supplement the visible lane line detection data, so as to better serve the autonomous driving field and promote the progress of the technology.

The purpose of our study is to establish a real vehicle-mounted infrared lane line detection database to verify the actual effect of lane line detection algorithm on real roads. This data set is collected in different seasons (spring, summer, autumn and winter) and various road scenarios (such as national roads and highways, etc.) through the infrared sensor developed by our company. The image resolution of the dataset is 640*512, we use points to annotate lane lines in annotated JSON file, take the upper left of the picture as the origin point, and then the points belonging to the lane line are marked(the points: [x, y] in annotated JSON file). And we use different labels (1,2,3...) for lane line points belonging to different lanes, you can adjust the format of the dataset according to their own needs. We sincerely hope that this infrared lane line data set will help you in your work and advance the development of the autonomous driving industry

The researchers

The study was conducted by:

Maofeng Tang ( maofeng.tang@raysense.com )

Zhaofei Xu ( zfxu@raysense.com )

Shuigen Wang ( shuigen.wang@raysense.com )

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