  As the scale of object detection dataset is smaller than that of image
recognition dataset ImageNet, transfer learning has become a basic training
method for deep learning object detection models, which will pretrain the
backbone network of object detection model on ImageNet dataset to extract
features for classification and localization subtasks. However, the
classification task focuses on the salient region features of object, while the
location task focuses on the edge features of object, so there is certain
deviation between the features extracted by pretrained backbone network and the
features used for localization task. In order to solve this problem, a
decoupled self attention(DSA) module is proposed for one stage object detection
models in this paper. DSA includes two decoupled self-attention branches, so it
can extract appropriate features for different tasks. It is located between FPN
and head networks of subtasks, so it is used to extract global features based
on FPN fused features for different tasks independently. Although the network
of DSA module is simple, but it can effectively improve the performance of
object detection, also it can be easily embedded in many detection models. Our
experiments are based on the representative one-stage detection model
RetinaNet. In COCO dataset, when ResNet50 and ResNet101 are used as backbone
networks, the detection performances can be increased by 0.4% AP and 0.5% AP
respectively. When DSA module and object confidence task are applied in
RetinaNet together, the detection performances based on ResNet50 and ResNet101
can be increased by 1.0% AP and 1.4% AP respectively. The experiment results
show the effectiveness of DSA module. Code is at:
https://github.com/chenzuge1/DSANet.git.
