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Mapillary releases traffic sign recognition dataset to teach autonomous vehicles to understand traffic signs

Mapillary has launched traffic sign recognition dataset to teach autonomous vehicles to understand traffic signs. It is known that Mapillary is the street-level imagery platform that uses computer vision to automate and scale mapping. According to the company it is the world’s largest and most diverse dataset publicly available.

Named the Mapillary Traffic Sign Dataset, the dataset consists of 100,000 images from all over the world and the images boast high variability in everything from weather and times of day when the images have been taken, to camera sensors and viewpoints. Although traffic sign recognition technology is fairly common already, this is the first time such a large and diverse dataset has been launched for anyone to license to train their own traffic sign recognition systems.

Emil Dautovic, VP of Automotive at Mapillary, says:
“Carmakers typically go out and get their own data to train their algorithms, but that means that they have low levels of variability in their training data. When it comes to teaching cars to see, more diverse input data means better results. There hasn’t been anything like the Mapillary Traffic Sign Dataset available on the market before, and that’s why we built it.”

Mapillary has a collaborative approach, meaning that all 570 million images on the Mapillary platform have been uploaded by people and companies from all over the world. 100,000 of these images have been selected for the Mapillary Traffic Sign Dataset. More than 300 different traffic sign classes have been verified and annotated, resulting in more than 320,000 labeled traffic signs across the images. Over 52,000 images have been fully verified and annotated by humans, with the remaining images annotated partially, using Mapillary’s computer vision technology.

The news is announced just months after research showed that cheap cameras have the potential to catch up with LiDAR in teaching autonomous vehicles to understand their surroundings, something that would reduce the cost of an autonomous vehicle by tens of thousands of dollars. Mapillary’s new dataset tackles a different part of the problem of perception by addressing traffic sign recognition, but Dautovic says the diversity in the dataset is key in moving towards camera-based solutions.

Dautovic explains: “The strength in the Mapillary Traffic Sign Dataset really lies in the diversity of the input data. There are only a few other datasets on the market, and none of them has imagery from all over the world, simply because it would take too much effort for one, single player to get images from such a diverse set of locations on a global scale. We don’t actually need to do that at Mapillary, since all the images have been uploaded to the Mapillary platform by people across the entire world. With this dataset, we’re hoping to come closer to solving the problem of self-driving from cameras only, one step at a time.”

Source: Press Release


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