Cascade R-CNN: Delving Into High Quality Object Detection

Abstract

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.

PCL: Proposal Cluster Learning for Weakly Supervised Object Detection

“…Object detection has attracted a great deal of attention in recent years [4,13,14,16,19,20,27,28,30,38,39,43,47,48,56]. One popular direction for recent object detection is proposal-based object detectors (a.k.a.…”

Occluded Pedestrian Detection with Visible IoU and Box Sign Predictor

“…Compared with baseline, our model outputs more accurate boxes and detects pedestrians with heavy occlusion. equation (5), the performance reaches 12.96%. Though the sign prediction loss indeed helps improving the performance, one can argue that it is because the loss involved with box prediction is increased and the sign predictor structure is not necessary.…”

“…It only selects the proper samples which fall in the desired scale range under different pyramids for training. Cascade R-CNN [5] adopts cascaded classifiers where training samples with increasingly higher overlap with ground truths are fed. Online hard example mining (OHEM) [6] dynamically chooses the samples with the highest loss in a batch to achieve better convergence and performance.…”

IMMVP: An Efficient Daytime and Nighttime On-Road Object Detector

“…Our design strategy is to select the model of the highest accuracy from the existing state-of-the-art ones at first and then improve the efficiency of the model. Among the existing models, Cascade R-CNN [6] with ResNeXt-101 [10] backbone has the best accuracy on MS COCO dataset [11]. To further boost the performance, we add Feature Pyramid Network (FPN) [5] to the backbone of the Cascade R-CNN model so that features at different scales can be extracted better.…”