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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 largescale VoVNet and apply them to one-stage and two-stage object detectors. Our VoVNet based detectors outperform DenseNet based ones with 2× faster speed and the energy consumptions are reduced by 1.6× -4.1×. 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. Inception-V4 [24], ResNet [7], and DenseNet [9], it has become mainstream in object detector to adopt the modern state-of-the-art CNN models as feature extractor. As DenseNet is reported to achieve state-of-the-art performance in the classification task recently, it is natural to attempt to expand its usage to detection tasks. In our experiment (Table 4), we find that the DenseNet based detectors with fewer parameters and FLOPs outperform the detectors with ResNet, which is most widely used for the backbone of object detections. The main difference between ResNet and DenseNet is the way they aggregate their features; ResNet aggregates the features from shallower by summation while DenseNet does it by concatenation. As mentioned by Zhu et al. [32], arXiv:1904.09730v1 [cs.CV]

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The concurrent enhancement of the short-circuit current (JSC) and open-circuit voltage (VOC) is a key problem in the preparation of efficient organic solar cells (OSCs). In this paper, we report...

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