Recent researches attempt to improve the detection performance by adopting
the idea of cascade for single-stage detectors. In this paper, we analyze and
discover that inconsistency is the major factor limiting the performance. The
refined anchors are associated with the feature extracted from the previous
location and the classifier is confused by misaligned classification and
localization. Further, we point out two main designing rules for the cascade
manner: improving consistency between classification confidence and
localization performance, and maintaining feature consistency between different
stages. A multistage object detector named Cas-RetinaNet, is then proposed for
reducing the misalignments. It consists of sequential stages trained with
increasing IoU thresholds for improving the correlation, and a novel Feature
Consistency Module for mitigating the feature inconsistency. Experiments show
that our proposed Cas-RetinaNet achieves stable performance gains across
different models and input scales. Specifically, our method improves RetinaNet
from 39.1 AP to 41.1 AP on the challenging MS COCO dataset without any bells or
whistles.

本文提出了一种名为 Cas-RetinaNet 的级联目标检测器来降低不一致性和提高性能。该算法包括按照递增的 IoU 阈值进行训练的多个级别和一个用于减轻特征不一致性的新特征一致性模块。实验表明我们的算法相对于 RetinaNet，在不采用其他技巧的情况下，将在具有挑战性的 MS COCO 数据集上的表现从 39.1 提升至 41.1。

Cascade RetinaNet: 单级目标检测的一致性维护

Cascade RetinaNet: Maintaining Consistency for Single-Stage Object  Detection

The design of complexity-aware cascaded detectors, combining features of very
different complexities, is considered. A new cascade design procedure is
introduced, by formulating cascade learning as the Lagrangian optimization of a
risk that accounts for both accuracy and complexity. A boosting algorithm,
denoted as complexity aware cascade training (CompACT), is then derived to
solve this optimization. CompACT cascades are shown to seek an optimal
trade-off between accuracy and complexity by pushing features of higher
complexity to the later cascade stages, where only a few difficult candidate
patches remain to be classified. This enables the use of features of vastly
different complexities in a single detector. In result, the feature pool can be
expanded to features previously impractical for cascade design, such as the
responses of a deep convolutional neural network (CNN). This is demonstrated
through the design of a pedestrian detector with a pool of features whose
complexities span orders of magnitude. The resulting cascade generalizes the
combination of a CNN with an object proposal mechanism: rather than a
pre-processing stage, CompACT cascades seamlessly integrate CNNs in their
stages. This enables state of the art performance on the Caltech and KITTI
datasets, at fairly fast speeds.

通过复杂度感知级联训练（CompACT）算法进行级联学习，以实现在较少返回时间内优化级联检测器的准确率与复杂度。在深度卷积神经网络响应和目标建议机制基础上，实现了具有不同复杂度特征池的行人检测器，并在 Caltech 和 KITTI 数据集上取得了最先进的表现。