基于高分辨率航拍图像深度学习的海鸟检测

Lynda Ben Boudaoud, F. Maussang, R. Garello, Alexis Chevallier
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引用次数: 20

摘要

本文的总体目标是寻找一种基于海洋航空图像的鸟类自动检测和计数方法。现有文献中的大部分工作都是基于启发式的手工特征设计,这在大多数情况下会影响分类的有效性(分类的准确性)和效率(花费大量时间)。在本文中,我们提出了一种基于系统特征学习的分类方法,采用了一种新的深度卷积神经网络(CNN)架构。通过这种架构,通过监督学习利用JONATHAN数据集的训练步骤,从多维原始输入图像中自动学习特征。性能评估表明,基于CNN的架构分类器在JONATHAN测试数据上的准确率达到95%,整体检测方法在真实图像上的分类率(真阳性:鸟类)达到98%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Marine Bird Detection Based on Deep Learning using High-Resolution Aerial Images
The overarching goal of this paper is to find an automatic bird detection and counting method on aerial images of the ocean. Most of the existing works in the literature are based on heuristic handcrafted feature design, which in most cases affect the effectiveness (the accuracy of classification) and the efficiency (spending much time). In this paper, we propose a method built on a systematic feature learning based classification adopting a new deep Convolutional Neural Network (CNN) architecture. Through this architecture, the feature learning is automated from a multi-dimensional raw input images, by a training step leveraging the JONATHAN dataset via supervised learning. Performances evaluation show that the CNN based architecture classifier achieves an accuracy of 95% on the JONATHAN test data and the overall detection method achieves a classification rate (true positives: birds) of 98% on real images.
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