A Flying Bird Object Detection Method for Surveillance Video

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 article 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 multiscale (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 article, 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. This project is publicly available at https://github.com/Ziwei89/FBOD.