Abstract
Object detection is a computer vision field that has applications in several contexts ranging from biomedicine and agriculture to security. In the last years, several deep learning techniques have greatly improved object detection models. Among those techniques, we can highlight the YOLO approach, that allows the construction of accurate models that can be employed in real-time applications. However, as most deep learning techniques, YOLO has a steep learning curve and creating models using this approach might be challenging for non-expert users. In this work, we tackle this problem by constructing a suite of Jupyter notebooks that democratizes the construction of object detection models using YOLO. The suitability of our approach has been proven with a dataset of stomata images where we have achieved a mAP of 90.91%.
Abstract (translated)
物体检测是一种计算机视觉领域，在生物医学，农业和安全等多种环境中具有应用。在过去几年中，一些深度学习技术极大地改进了对象检测模型。在这些技术中，我们可以强调YOLO方法，该方法允许构建可用于实时应用的精确模型。然而，作为大多数深度学习技术，YOLO具有陡峭的学习曲线，并且使用这种方法创建模型对于非专家用户可能是具有挑战性的。在这项工作中，我们通过构建一套Jupyter笔记本来解决这个问题，这些笔记本使用YOLO对对象检测模型的构建进行民主化。我们的方法的适用性已经通过气孔图像数据集得到证实，我们已经实现了90.91％的mAP。
URL
https://arxiv.org/abs/1809.03322
