ABSTRACT One of the current challenges is to reduce collisions between vehicles and animals on roads, such accidents resulting in environmental imbalance and large expenditures in public coffers. This paper presents the components of a simple animal detection system and also a methodology for animals detection in images provided by cameras installed on the roads. This methodology allows the features extraction of regions of the image and the use of Machine Learning (ML) techniques to classify the areas into two classes: animal and non-animal. Two ML techniques were compared using synthetic images, traversing the pixels of the image using five distinctive approaches. Results show that the KNN learning model is more reliable than Random Forest to identify animals on roads accurately.

中文翻译：

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使用机器学习的动物检测系统的建议

摘要 当前的挑战之一是减少道路上车辆和动物之间的碰撞，此类事故导致环境失衡和公共金库的巨额支出。本文介绍了一个简单的动物检测系统的组成部分，以及一种在道路上安装的摄像头提供的图像中检测动物的方法。这种方法允许提取图像区域的特征，并使用机器学习 (ML) 技术将区域分为两类：动物和非动物。使用合成图像比较了两种 ML 技术，使用五种不同的方法遍历图像的像素。结果表明，在准确识别道路上的动物方面，KNN 学习模型比随机森林更可靠。