Seeing Darker-Skinned Pedestrians Children and people with darker skin face higher street risks with object detectors, research finds.

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2 min read
Screenshot of a pedestrian detector

In a study, models used to detect people walking on streets and sidewalks performed less well on adults with darker skin and children of all skin tones.

What’s new: Xinyui Li, Zhenpeng Chen, and colleagues at Peking University, University College London, and King’s College London evaluated eight widely used object detectors for bias with respect to skin color, age, and gender.

Key insight: When it comes to detecting pedestrians, biases with respect to demographic characteristics can be a life-and-death matter. Evaluating them requires a dataset of pedestrians labeled according to characteristics that might influence detection. Skin color, age, and gender are important human differences that can affect a vision model’s performance, especially depending on lighting conditions.

How it works: The authors collected over 8,000 photos from four datasets of street scenes. They annotated each image with labels for skin tone (light or dark), age group (child or adult), and gender (male or female). They tested four general-purpose object detectors: YOLOXRetinaNetFaster R-CNN, and Cascade R-CNN â€” and four pedestrian-specific detectors — ALFNetCSPMGAN, and PRNet â€” on their dataset. They evaluated performance between perceived skin tone, age, and gender groups and under different conditions of brightness, contrast, and weather.

Results: The study revealed significant fairness issues related to skin tone and age.

  • Six models detected people with light and dark skin tones equally well, but two — YOLOX and RetinaNet — were 30.71 and 28.03 percent less likely to detect darker-skinned people. In all cases, darker-skinned pedestrians were less likely to be detected under conditions of low contrast and low brightness.
  • All eight models showed worse performance with children than adults For instance, YOLOX detected children 26.06 less often, while CSP detected children 12.68 percent less often. On average, the models failed to detect 46.57 percent of children, but only 26.91 percent of adults.
  • Most of the models performed equally well regardless of gender. However, all eight had difficulty detecting women in the EuroCity-Night dataset, which contains photos shot after dark.

Behind the news: Previous work has shown that computer vision models can harbor biases that make them less likely to recognize individuals of certain types. In 2019, MIT showed that commercial face recognition performed worse on women and darker skinned individuals. A plethora of work evaluates bias in datasets typically used to train vision models. 

Why it matters: As more road vehicles gain self-driving capabilities and as expanded robotaxi services come to major cities, a growing number of pedestrians’ lives are in the hands of computer vision algorithms. Auto makers don’t disclose what pedestrian detection systems they use or the number of real-world accidents involving self-driving cars. But co-author Jie Zhang claims that the proprietary systems used in self-driving cars are “usually built upon the existing open-source models,” and “we can be certain that their models must also have similar issues.”

We’re thinking: Computer vision isn’t the only technology used by self-driving cars to detect objects. Most self-driving car manufacturers rely on lidar and radar in addition to cameras. Those technologies are blind to color and gender differences and, in the view of many engineers, make better choices for this application.


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