用Swin Transformer优化鸟类小目标检测，提升特征提取能力
Small Object Detection for Birds with Swin Transformer
- 设计分层颈部结构，结合Swin Transformer增强小目标特征
- 调整窗口大小至2，使小物体检测的mAP显著提升
- 专为稀疏小目标（如鸟类）设计，适合生态监测等场景
小目标检测在图像中尤为困难，尤其因尺寸小、模糊、遮挡等问题。现有方法多针对密集小目标（如人群或遥感远物），但对稀疏小目标（如鸟类）训练样本少，难以学习有效特征。本文提出专用于鸟类检测的方法，改进骨干网络与预测头之间的颈部结构，采用Swin Transformer进行特征上采样，并调整移位窗口大小以适配小目标。实验表明，基于Swin Transformer的颈部结构结合CenterNet，在改变窗口大小后表现优异；更小的窗口尺寸（默认2）显著提升小目标的mAP。
原文摘要 · Abstract (English)
Object detection is the task of detecting objects in an image. In this task, the detection of small objects is particularly difficult. Other than the small size, it is also accompanied by difficulties due to blur, occlusion, and so on. Current small object detection methods are tailored to small and dense situations, such as pedestrians in a crowd or far objects in remote sensing scenarios. However, when the target object is small and sparse, there is a lack of objects available for training, making it more difficult to learn effective features. In this paper, we propose a specialized method for detecting a specific category of small objects; birds. Particularly, we improve the features learned by the neck; the sub-network between the backbone and the prediction head, to learn more effective features with a hierarchical design. We employ Swin Transformer to upsample the image features. Moreover, we change the shifted window size for adapting to small objects. Experiments show that the proposed Swin Transformer-based neck combined with CenterNet can lead to good performance by changing the window sizes. We further find that smaller window sizes (default 2) benefit mAPs for small object detection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。