Most of the recent successful methods in accurate object detection and
localization used some variants of R-CNN style two stage Convolutional Neural
Networks (CNN) where plausible regions were proposed in the first stage then
followed by a second stage for decision refinement. Despite the simplicity of
training and the efficiency in deployment, the single stage detection methods
have not been as competitive when evaluated in benchmarks consider mAP for high
IoU thresholds. In this paper, we proposed a novel single stage end-to-end
trainable object detection network to overcome this limitation. We achieved
this by introducing Recurrent Rolling Convolution (RRC) architecture over
multi-scale feature maps to construct object classifiers and bounding box
regressors which are "deep in context". We evaluated our method in the
challenging KITTI dataset which measures methods under IoU threshold of 0.7. We
showed that with RRC, a single reduced VGG-16 based model already significantly
outperformed all the previously published results. At the time this paper was
written our models ranked the first in KITTI car detection (the hard level),
the first in cyclist detection and the second in pedestrian detection. These
results were not reached by the previous single stage methods. The code is
publicly available.

本文提出一种新的单级端到端可训练目标检测网络，采用循环滚动卷积 (RRC) 架构在多尺度特征图上构建物体分类器和边界框回归器来克服单级检测方法的限制。在挑战性的 KITTI 数据集上评估了我们的方法，证明 RRC 已经在 KITTI 车辆检测 (困难级别) 中超越了之前所有已发布的结果。