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

Flying Bird Object Detection Algorithm in Surveillance Video

Anomaly detection attempts at finding examples that deviate from the expected
behaviour. Usually, anomaly detection is tackled from an unsupervised
perspective because anomalous labels are rare and difficult to acquire.
However, the lack of labels makes the anomaly detector have high uncertainty in
some regions, which usually results in poor predictive performance or low user
trust in the predictions. One can reduce such uncertainty by collecting
specific labels using Active Learning (AL), which targets examples close to the
detector's decision boundary. Alternatively, one can increase the user trust by
allowing the detector to abstain from making highly uncertain predictions,
which is called Learning to Reject (LR). One way to do this is by thresholding
the detector's uncertainty based on where its performance is low, which
requires labels to be evaluated. Although both AL and LR need labels, they work
with different types of labels: AL seeks strategic labels, which are evidently
biased, while LR requires i.i.d. labels to evaluate the detector's performance
and set the rejection threshold. Because one usually has a unique label budget,
deciding how to optimally allocate it is challenging. In this paper, we propose
a mixed strategy that, given a budget of labels, decides in multiple rounds
whether to use the budget to collect AL labels or LR labels. The strategy is
based on a reward function that measures the expected gain when allocating the
budget to either side. We evaluate our strategy on 18 benchmark datasets and
compare it to some baselines.

本文提出了一种基于奖励函数的混合策略，用于决定在多轮中分配标签预算以收集主动学习标签或学习拒绝标签，以优化异常检测性能。应用于 18 个基准数据集，结果表明该策略优于基线方法。