A Flying Bird Object Detection Method for Surveillance Video
- URL: http://arxiv.org/abs/2401.03749v3
- Date: Thu, 29 Aug 2024 08:52:40 GMT
- Title: A Flying Bird Object Detection Method for Surveillance Video
- Authors: Ziwei Sun, Zexi Hua, Hengchao Li, Yan Li,
- Abstract summary: This paper proposes a Flying Bird Object Detection method for Surveillance Video (FBOD-SV)
The FBOD-SV is validated using experimental datasets of flying bird objects in traction substation surveillance videos.
The experimental results show that the FBOD-SV effectively improves the detection performance of flying bird objects in surveillance video.
- Score: 9.597393200515377
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Aiming at the specific characteristics of flying bird objects in surveillance video, such as the typically non-obvious features in single-frame images, small size in most instances, and asymmetric shapes, 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 followed by up-sampling is designed, which utilizes 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 performance of the FBOD-SV is validated using experimental datasets of flying bird objects in traction substation surveillance videos. The experimental results show that the FBOD-SV effectively improves the detection performance of flying bird objects in surveillance video.
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