  In object detection, an intersection over union (IoU) threshold is required
to define positives and negatives. An object detector, trained with low IoU
threshold, e.g. 0.5, usually produces noisy detections. However, detection
performance tends to degrade with increasing the IoU thresholds. Two main
factors are responsible for this: 1) overfitting during training, due to
exponentially vanishing positive samples, and 2) inference-time mismatch
between the IoUs for which the detector is optimal and those of the input
hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, is
proposed to address these problems. It consists of a sequence of detectors
trained with increasing IoU thresholds, to be sequentially more selective
against close false positives. The detectors are trained stage by stage,
leveraging the observation that the output of a detector is a good distribution
for training the next higher quality detector. The resampling of progressively
improved hypotheses guarantees that all detectors have a positive set of
examples of equivalent size, reducing the overfitting problem. The same cascade
procedure is applied at inference, enabling a closer match between the
hypotheses and the detector quality of each stage. A simple implementation of
the Cascade R-CNN is shown to surpass all single-model object detectors on the
challenging COCO dataset. Experiments also show that the Cascade R-CNN is
widely applicable across detector architectures, achieving consistent gains
independently of the baseline detector strength. The code will be made
available at https://github.com/zhaoweicai/cascade-rcnn.
