基于图像的野生动物检测与识别综述

野生动物监测对于野生动物保护和生态系统维护至关重要, 而野生动物的检测与识别是实现监测的核心技术. 近年来, 随着计算机视觉技术的迅速发展和广泛应用, 基于图像的非接触式方法在野生动物监测领域引起了广泛的关注, 研究人员提出了各种方法来解决该领域的不同问题. 然而, 野外环境的复杂性使得对野生动物进行精确检测和识别仍具有一定的挑战. 为了推动该领域的研究, 本文对现有的基于图像的野生动物监测方法进行了综述, 主要包括3个部分: 野生动物图像获取方法、野生动物影像预处理方法以及野生动物检测与识别算法. 文章按照图像数据集和野生动物检测与识别算法的不同处理机制对这些方法进行了探讨和分类. 最后, 本文对基于深度学习的野生动物监测研究热点与存在问题进行了分析和总结, 并对未来的研究重点提出了展望.

Review on Image-based Wildlife Detection and Recognition

Wildlife monitoring is essential for wildlife conservation and ecosystem maintenance, and wildlife detection and identification is the core technology to achieve monitoring. In recent years, with the rapid development and widespread application of computer vision technology, image-based non-contact methods have attracted extensive attention in the field of wildlife monitoring, and researchers have proposed various methods to solve different problems in this field. However, the complexity of wild environment still poses challenges for accurate detection and identification of wildlife. In order to promote research in this field, the existing image-based wildlife monitoring methods are reviewed in this study, which mainly include three sections: wildlife image acquisition methods, wildlife image preprocessing methods, and wildlife detection and recognition algorithms. These methods are discussed and classified according to the different processing mechanisms of image datasets and wildlife detection and recognition algorithms. Finally, the research hotspots and existing problems of wildlife monitoring based on deep learning are analyzed and summarized, and the prospect for future research priorities is proposed in the study.