Current top-performing object detectors depend on deep CNN backbones, such as
ResNet-101 and Inception, benefiting from their powerful feature
representations but suffering from high computational costs. Conversely, some
lightweight model based detectors fulfil real time processing, while their
accuracies are often criticized. In this paper, we explore an alternative to
build a fast and accurate detector by strengthening lightweight features using
a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs)
in human visual systems, we propose a novel RF Block (RFB) module, which takes
the relationship between the size and eccentricity of RFs into account, to
enhance the feature discriminability and robustness. We further assemble RFB to
the top of SSD, constructing the RFB Net detector. To evaluate its
effectiveness, experiments are conducted on two major benchmarks and the
results show that RFB Net is able to reach the performance of advanced very
deep detectors while keeping the real-time speed. Code is available at
https://github.com/ruinmessi/RFBNet.Comment: Accepted by ECCV 201