A Flying Bird Object Detection Method for Surveillance Video

TL;DR

This paper introduces a novel detection method tailored for flying bird objects in surveillance videos, addressing challenges like small size and irregular shapes through specialized feature aggregation and dynamic label strategies.

Contribution

It proposes the FBOD-SV framework with a new correlation attention feature aggregation module and a dynamic label allocation method for improved bird detection.

Findings

Enhanced detection accuracy for small, irregular flying bird objects

Effective handling of multi-scale bird features in surveillance videos

Improved performance demonstrated on real-world datasets

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.