  We analyzed the network structure of real-time object detection models and
found that the features in the feature concatenation stage are very rich.
Applying an attention module here can effectively improve the detection
accuracy of the model. However, the commonly used attention module or
self-attention module shows poor performance in detection accuracy and
inference efficiency. Therefore, we propose a novel self-attention module,
called 2D local feature superimposed self-attention, for the feature
concatenation stage of the neck network. This self-attention module reflects
global features through local features and local receptive fields. We also
propose and optimize an efficient decoupled head and AB-OTA, and achieve SOTA
results. Average precisions of 49.0% (71FPS, 14ms), 46.1% (85FPS, 11.7ms), and
39.1% (107FPS, 9.3ms) were obtained for large, medium, and small-scale models
built using our proposed improvements. Our models exceeded YOLOv5 by 0.8% --
3.1% in average precision.
