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.

本文提出了一种适用于监控视频中飞鸟对象的飞鸟对象检测方法，该方法通过多个连续图像帧上的飞鸟对象的相关性聚合特征，并利用下采样和上采样设计了一个具有大特征图层的飞鸟对象检测网络来检测特殊的多尺度（主要是小尺度）鸟类对象。最后，应用了SimOTA动态标签分配方法来解决不规则飞鸟对象引起的标签分配问题，实验结果表明本文提出的监控视频飞鸟对象检测方法有效地提高了飞鸟对象的检测性能。

监控视频中的飞行鸟类目标检测算法