cascade r-cnn paper

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 per- formance tends to degrade with increasing the IoU thresh- olds. Two main factors are responsible for this: 1) over- fitting during training, due to exponentially vanishing pos- itive samples, and 2) inference-time mismatch between the IoUs for which the detector is optimal and those of the in- put hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, is proposed to address these prob- lems. It consists of a sequence of detectors trained with increasing IoU thresholds, to be sequentially more selec- tive against close false positives. The detectors are trained stage by stage, leveraging the observation that the out- put of a detector is a good distribution for training the next higher quality detector. The resampling of progres- sively improved hypotheses guarantees that all detectors have a positive set of examples of equivalent size, reduc- ing 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 challeng- ing COCO dataset. Experiments also show that the Cas- cade R-CNN is widely applicable across detector architec- tures, achieving consistent gains independently of the base- line detector strength. The code will be made available at https://github.com/zhaoweicai/cascade-rcnn.