Guo Zhaojin, He Zheng, Lyu Li, Mao Axiu, Huang Endai, Liu Kai
Department of Infectious Diseases and Public Health, City University of Hong Kong, Hong Kong SAR, China.
School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.
Animals (Basel). 2024 Jan 3;14(1):159. doi: 10.3390/ani14010159.
The overpopulation of feral pigeons in Hong Kong has significantly disrupted the urban ecosystem, highlighting the urgent need for effective strategies to control their population. In general, control measures should be implemented and re-evaluated periodically following accurate estimations of the feral pigeon population in the concerned regions, which, however, is very difficult in urban environments due to the concealment and mobility of pigeons within complex building structures. With the advances in deep learning, computer vision can be a promising tool for pigeon monitoring and population estimation but has not been well investigated so far. Therefore, we propose an improved deep learning model (Swin-Mask R-CNN with SAHI) for feral pigeon detection. Our model consists of three parts. Firstly, the Swin Transformer network (STN) extracts deep feature information. Secondly, the Feature Pyramid Network (FPN) fuses multi-scale features to learn at different scales. Lastly, the model's three head branches are responsible for classification, best bounding box prediction, and segmentation. During the prediction phase, we utilize a Slicing-Aided Hyper Inference (SAHI) tool to focus on the feature information of small feral pigeon targets. Experiments were conducted on a feral pigeon dataset to evaluate model performance. The results reveal that our model achieves excellent recognition performance for feral pigeons.
香港野生鸽子数量过多已严重扰乱城市生态系统，凸显了采取有效策略控制其数量的迫切需求。一般来说，应在准确估计相关区域野生鸽子数量后定期实施控制措施并重新评估，然而，由于鸽子在复杂建筑结构中的隐蔽性和流动性，在城市环境中进行准确估计非常困难。随着深度学习的发展，计算机视觉有望成为鸽子监测和数量估计的工具，但目前尚未得到充分研究。因此，我们提出了一种改进的深度学习模型（带有SAHI的Swin-Mask R-CNN）用于野生鸽子检测。我们的模型由三部分组成。首先，Swin Transformer网络（STN）提取深度特征信息。其次，特征金字塔网络（FPN）融合多尺度特征以在不同尺度上进行学习。最后，模型的三个头部分支负责分类、最佳边界框预测和分割。在预测阶段，我们使用切片辅助超推理（SAHI）工具来聚焦小野生鸽子目标的特征信息。在一个野生鸽子数据集上进行了实验以评估模型性能。结果表明，我们的模型对野生鸽子具有出色的识别性能。
