首页 > 分享 > 基于深度学习算法的鸟类及其栖息地识别——以北京翠湖国家城市湿地公园为例

基于深度学习算法的鸟类及其栖息地识别——以北京翠湖国家城市湿地公园为例

萌宠菠菠乐园
2024-11-26 10:49

摘要： 获取鸟类的种类、数量及生境信息是鸟类生态学研究的基础。近些年基于深度学习算法的人工智能技术发展迅速，弥补了传统野外调查方法的缺陷，为鸟类生态学研究提供了智慧化手段。本文选取北京翠湖国家城市湿地公园4处观测点，通过搭建具有自适应损失函数的ResNet34双任务网络模型，实现同时识别鸟种及其栖息地类型。结果表明：基于模型共记录鸟类10种，以鸿雁、苍鹭、普通鸬鹚和绿头鸭为主，其中，夜鹭和苍鹭以树木作为主要栖息地，鸿雁和绿头鸭以水面作为主要栖息地，普通鸬鹚、斑嘴鸭和小白鹭以水中立木等人工生境作为主要栖息地，喜鹊则主要以地面作为栖息地，赤麻鸭和鸳鸯的栖息地较为广泛，在地面、人工生境、水面区域均有栖息；识别模型的鸟种识别准确率达95.62%，栖息地识别准确率达97.20%；识别方案采用基于深度学习技术的鸟类图像采集方法代替人工数据采集手段，并首次使用“物种+栖息地”的双任务分支结构，对物种及其栖息地两类信息同时进行识别；模型提高了数据采集效率，保证了数据采集的客观性和准确性，实现了鸟类生态学研究与人工智能技术的有效结合，对生态学研究方法的演进具有参考意义。

关键词: 人工智能, 深度学习, 鸟类多样性, 栖息地

Abstract: Information on species identity, abundance, and habitat is the fundamental requirement in bird ecology research. The rapid development of artificial intelligence technology based on deep learning algorithm has made up for the shortcomings of traditional field investigation methods, providing an intelligent means for bird ecology research. By building a ResNet34 dualtask network model with adaptive loss function, a bird and habitat recognition model was built to realize the simultaneous identification of bird species and habitat types across four observation sites in Beijing Cuihu National Urban Wetland Park. A total of 10 bird species were recorded based on this model, including swan goose, gray heron, great cormorant and mallard. Night heron and gray heron use trees as main habitats. Swan goose and mallard use water as main habitats. Great cormorant, eastern spot-billed duck, and little egret use artificial habitats such as standing trees in water as main habitats. Common magpies mainly use the ground as habitat. Ruddy shelduck and mandarin duck have a wide range of habitats, which are distributed on the ground, artificial habitats, and water. The recognition accuracy of the proposed model reaches 95.62% for bird species and 97.20% for habitat types. The bird image acquisition method based on deep learning technology was used to replace the artificial data collection means, and the dual-task branch structure of “species + habitat” was used for the first time to identify species and habitat, which greatly improved the efficiency and ensures the objectivity and accuracy of data collection. It realizes the effective combination of avian ecology research and artificial intelligence, which has reference significance for the evolution of ecological research methods.

Key words: artificial intelligence, deep learning, avian diversity, habitat