  Aiming at the characteristics of the flying bird object in surveillance
video, such as the single frame image feature is not obvious, the size is small
in most cases, and asymmetric, this paper proposes a Flying Bird Object
Detection method for Surveillance Video (FBOD-SV). Firstly, a new feature
aggregation module, the Correlation Attention Feature Aggregation
(Co-Attention-FA) module, is designed to aggregate the features of the flying
bird object according to the bird object's correlation on multiple consecutive
frames of images. Secondly, a Flying Bird Object Detection Network (FBOD-Net)
with down-sampling and then up-sampling is designed, which uses a large feature
layer that fuses fine spatial information and large receptive field information
to detect special multi-scale (mostly small-scale) bird objects. Finally, the
SimOTA dynamic label allocation method is applied to One-Category object
detection, and the SimOTA-OC dynamic label strategy is proposed to solve the
difficult problem of label allocation caused by irregular flying bird objects.
In this paper, the algorithm's performance is verified by the experimental data
set of the surveillance video of the flying bird object of the traction
substation. The experimental results show that the surveillance video flying
bird object detection method proposed in this paper effectively improves the
detection performance of flying bird objects.

Computer Vision and Pattern Recognition