Li Chaoyang, He Zhipeng, Lu Kai, Fang Chaoyang
Jiangxi Protected Area Construction Center, Nanchang 330006, China.
Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China.
Sensors (Basel). 2025 Jan 6;25(1):291. doi: 10.3390/s25010291.
Bird species detection is critical for applications such as the analysis of bird population dynamics and species diversity. However, this task remains challenging due to local structural similarities and class imbalances among bird species. Currently, most deep learning algorithms focus on designing local feature extraction modules while ignoring the importance of global information. However, this global information is essential for accurate bird species detection. To address this limitation, we propose BSD-Net, a bird species detection network. BSD-Net efficiently learns local and global information in pixels to accurately detect bird species. BSD-Net consists of two main components: a dual-branch feature mixer (DBFM) and a prediction balancing module (PBM). The dual-branch feature mixer extracts features from dichotomous feature segments using global attention and deep convolution, expanding the network's receptive field and achieving a strong inductive bias, allowing the network to distinguish between similar local details. The prediction balance module balances the difference in feature space based on the pixel values of each category, thereby resolving category imbalances and improving the network's detection accuracy. The experimental results using two public benchmarks and a self-constructed Poyang Lake Bird dataset demonstrate that BSD-Net outperforms existing methods, achieving 45.71% and 80.00% mAP50 with the CUB-200-2011 and Poyang Lake Bird datasets, respectively, and 66.03% AP with FBD-SV-2024, allowing for more accurate location and species information for bird detection tasks in video surveillance.
鸟类物种检测对于诸如鸟类种群动态分析和物种多样性分析等应用至关重要。然而，由于鸟类物种之间存在局部结构相似性和类别不平衡，这项任务仍然具有挑战性。目前，大多数深度学习算法专注于设计局部特征提取模块，而忽略了全局信息的重要性。然而，这种全局信息对于准确的鸟类物种检测至关重要。为了解决这一局限性，我们提出了BSD-Net，一种鸟类物种检测网络。BSD-Net能够有效地学习像素中的局部和全局信息，以准确检测鸟类物种。BSD-Net由两个主要组件组成：双分支特征混合器（DBFM）和预测平衡模块（PBM）。双分支特征混合器使用全局注意力和深度卷积从二分特征段中提取特征，扩大网络的感受野并实现强大的归纳偏差，使网络能够区分相似的局部细节。预测平衡模块基于每个类别的像素值平衡特征空间中的差异，从而解决类别不平衡问题并提高网络的检测精度。使用两个公共基准和自行构建的鄱阳湖鸟类数据集进行的实验结果表明，BSD-Net优于现有方法，在CUB-200-2011和鄱阳湖鸟类数据集上分别实现了45.71%和80.00%的mAP50，在FBD-SV-2024上实现了66.03%的AP，能够为视频监控中的鸟类检测任务提供更准确的位置和物种信息。
