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

Flying Bird Object Detection Algorithm in Surveillance Video