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基于改进Cascade R-CNN的雪豹物种水平的自动检测方法
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基于改进Cascade R-CNN的雪豹物种水平的自动检测方法
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Journal Article
基于改进Cascade R-CNN的雪豹物种水平的自动检测方法
2022
Overview
雪豹(Panthera uncia)的皮毛具有较强的隐蔽性,红外相机监测图像中雪豹和背景较为相似,而且监测图像中雪豹的尺寸大小不一,为了提升雪豹检测的准确率,采用3个不同的检测器进行级联,在特征提取网络中引入特征金字塔结构改进Cascade R-CNN模型实现了雪豹的自动检测。以项目组采集的雪豹监测图像为数据集的评估结果表明,无论是白天/黑夜图像,还是多种不同尺寸雪豹同时出现的图像,该方法都可以较好地实现雪豹的识别及定位,平均准确率达93.0%,对比Faster R-CNN和SSD-300(Single Shot MultiBox Detector 300)分别提升了9.0%和3.9%。将该模型应用于雪豹监测图像的自动筛选,可以极大地提高工作效率。
Publisher
Editorial Department of Chinese Journal of Wildlife
Subject
