基于伪红外图像训练的可靠夜熊和野猪检测

Keigo Fusaka, Yoichi Tomioka, Hiroshi Saito, Y. Kohira
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引用次数: 0

摘要

近年来,野生动物造成的事故和损害已经成为严重的问题。在早期阶段准确地发现野生动物已经变得很重要。利用深度学习技术检测夜间各种姿态的野生动物,需要足够数量的训练红外图像。在这项研究中,我们提出了一种方法来增加适当的训练样本,用于夜间野生动物的检测使用带注释的白天图像。我们采用了一种基于循环生成对抗网络(CycleGAN)的模型,能够从白天的图像中生成伪红外图像。在我们的实验中,我们将该方法应用于熊和野猪的检测。实验结果表明,该方法在不同姿态下的熊检测精度均有显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reliable Night Bear and Boar Detection Based on Training with Pseudo Infrared Images
In recent years, accidents and damages caused by wild animals have been serious prob- lems. It has become important to detect wild animals accurately at an early stage. A sufficient number of training infrared images is required to detect wild animals taking various postures at night time using deep learning techniques. In this study, we propose a method to increase appropriate training samples for night wild animal detection using annotated daytime images. We employ a model based on Cycle Generative Adversarial Network (CycleGAN) to be able to generate pseudo infrared images from daytime images. In our experiments, we apply the proposed method to bear and boar detection. The exper- imental results show that the proposed method achieves significant improvements in bear detection accuracy taking various postures.
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CiteScore
1.60
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