Title: Accurate Single Stage Detector Using Recurrent Rolling Convolution,
Abstract: 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.