Small Bird Detection using YOLOv7 with Test-Time Augmentation
This work addresses the problem of detecting small birds for ecological monitoring or similar applications, but it is incremental as it adapts existing methods to a specific dataset.
The paper tackled small bird detection by applying YOLOv7 with test-time augmentation, achieving a top score in the Development category with a public AP of 0.732 and a private AP of 27.2 at IoU=0.5.
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.