  Adder neural networks (AdderNets) have shown impressive performance on image
classification with only addition operations, which are more energy efficient
than traditional convolutional neural networks built with multiplications.
Compared with classification, there is a strong demand on reducing the energy
consumption of modern object detectors via AdderNets for real-world
applications such as autonomous driving and face detection. In this paper, we
present an empirical study of AdderNets for object detection. We first reveal
that the batch normalization statistics in the pre-trained adder backbone
should not be frozen, since the relatively large feature variance of AdderNets.
Moreover, we insert more shortcut connections in the neck part and design a new
feature fusion architecture for avoiding the sparse features of adder layers.
We present extensive ablation studies to explore several design choices of
adder detectors. Comparisons with state-of-the-arts are conducted on COCO and
PASCAL VOC benchmarks. Specifically, the proposed Adder FCOS achieves a 37.8\%
AP on the COCO val set, demonstrating comparable performance to that of the
convolutional counterpart with an about $1.4\times$ energy reduction.
