With the continuous development and applications of autonomous driving, more and more scientific researches pay attention to autonomous driving algorithms such as mapping, navigation, planning, control, and obstacle avoidance, etc. By going deeper, we find that object detection is the foundation for carrying on other tasks. Hence, in this section, we plan to provide a number of labeled and unlabeled paired/unpaired on-vehicle visible and infrared images for detecting pedestrians, vehicles and non-motor vehicles.

Compared with visible sensors, infrared sensors could provide more comprehensive and extensive information in night autonomous driving. Actually, according to some surveys, there are more than 50% traffic accidents happened during night. And the main reason is terrible environment light condition, which influences drivers’ decisions. However, if infrared imaging devices could be installed on vehicles, drivers could have a good view as the same as daytime, with everything clear. Then, a large number of traffic accidents could be avoided in the future. Up to now, this is the only one public dataset by publishing paired visible and infrared images. We hope that our database will be a valuable resource, so that all the research communities around the world could design better models for autonomous driving.

The researchers

The study was conducted by:

Gangqiang Li ( gqli@raysense.com )

Jiansheng Wang ( jiansheng.wang@raysense.com )

Shuigen Wang ( shuigen.wang@raysense.com )

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