  As DenseNet conserves intermediate features with diverse receptive fields by
aggregating them with dense connection, it shows good performance on the object
detection task. Although feature reuse enables DenseNet to produce strong
features with a small number of model parameters and FLOPs, the detector with
DenseNet backbone shows rather slow speed and low energy efficiency. We find
the linearly increasing input channel by dense connection leads to heavy memory
access cost, which causes computation overhead and more energy consumption. To
solve the inefficiency of DenseNet, we propose an energy and computation
efficient architecture called VoVNet comprised of One-Shot Aggregation (OSA).
The OSA not only adopts the strength of DenseNet that represents diversified
features with multi receptive fields but also overcomes the inefficiency of
dense connection by aggregating all features only once in the last feature
maps. To validate the effectiveness of VoVNet as a backbone network, we design
both lightweight and large-scale VoVNet and apply them to one-stage and
two-stage object detectors. Our VoVNet based detectors outperform DenseNet
based ones with 2x faster speed and the energy consumptions are reduced by 1.6x
- 4.1x. In addition to DenseNet, VoVNet also outperforms widely used ResNet
backbone with faster speed and better energy efficiency. In particular, the
small object detection performance has been significantly improved over
DenseNet and ResNet.

Computer Vision and Pattern Recognition