论文 Cascade R-CNN: High Quality Object Detection and Instance Segmentation

叁歲伎倆 2022-08-28 05:58 8阅读 0赞

[https://arxiv.org/abs/1906.09756][https_arxiv.org_abs_1906.09756]  
In object detection, the intersection over union (IoU) threshold isfrequently used to define positives/negatives. The threshold used to train adetector defines its \\textit\{quality\}. While the commonly used threshold of 0.5leads to noisy (low-quality) detections, detection performance frequentlydegrades for larger thresholds. This paradox of high-quality detection has twocauses: 1) overfitting, due to vanishing positive samples for large thresholds,and 2) inference-time quality mismatch between detector and test hypotheses. Amulti-stage object detection architecture, the Cascade R-CNN, composed of asequence of detectors trained with increasing IoU thresholds, is proposed toaddress these problems. The detectors are trained sequentially, using theoutput of a detector as training set for the next. This resamplingprogressively improves hypotheses quality, guaranteeing a positive training setof equivalent size for all detectors and minimizing overfitting. The samecascade is applied at inference, to eliminate quality mismatches betweenhypotheses and detectors. An implementation of the Cascade R-CNN without bellsor whistles achieves state-of-the-art performance on the COCO dataset, andsignificantly improves high-quality detection on generic and specific objectdetection datasets, including VOC, KITTI, CityPerson, and WiderFace. Finally,the Cascade R-CNN is generalized to instance segmentation, with nontrivialimprovements over the Mask R-CNN. To facilitate future research, twoimplementations are made available at\\url\{this https URL\} (Caffe) and\\url\{this https URL\} (Detectron).

[https_arxiv.org_abs_1906.09756]: https://arxiv.org/abs/1906.09756