[strken]

The two with significantly worse performance were RetinaNet and YOLOX. I don't really know anything about the field, but it's interesting they're both single stage performant models, while the slower but lower miss-rate RCNN variants are two-stage. It's interesting that the pedestrian-specific models are all worse than the general models at detecting people!

The conclusion is kind of weird: apparently their "findings reveal significant bias in the current pedestrian detectors" despite the bias being almost entirely within the single-pass general object detectors. And where it's statistically significant in the other models, the miss rate is low in both cases, and the effect is reversed! (Dry-weather Cascade-RCNN does better on dark-skin than light-skin, among others.)