  In this paper, we propose a method specifically aimed at improving small bird
detection for the Small Object Detection Challenge for Spotting Birds 2023.
Utilizing YOLOv7 model with test-time augmentation, our approach involves
increasing the input resolution, incorporating multiscale inference,
considering flipped images during the inference process, and employing weighted
boxes fusion to merge detection results. We rigorously explore the impact of
each technique on detection performance. Experimental results demonstrate
significant improvements in detection accuracy. Our method achieved a top score
in the Development category, with a public AP of 0.732 and a private AP of
27.2, both at IoU=0.5.
