论文标题
使用域随机化和转移学习在神经网络上的种子表型
Seed Phenotyping on Neural Networks using Domain Randomization and Transfer Learning
论文作者
论文摘要
种子表型是分析种子的形态特征的想法，以在各种环境条件下在发育，耐受性和产量方面预测种子的行为。这项工作的重点是对最先进的对象检测和定位神经网络的应用和可行性分析，蒙版R-CNN和Yolo（您只看一次），用于使用TensorFlow进行种子表型。这种努力的主要瓶颈之一是需要大量培训数据。虽然捕获了许多种子图像是嘲笑的，但还需要注释图像以指示图像上种子的边界并转换为神经网络能够消耗的数据格式。尽管可以免费提供手动执行注释任务的工具，但所需的时间却是巨大的。为了解决这种情况，考虑了域随机化的概念，即应用在包含模拟对象的图像上训练的模型的技术。此外，使用转移学习，即应用解决问题时应用知识的想法。这些网络经过受欢迎的ImageNet和可可数据集的预训练权重训练。作为工作的一部分，具有不同参数的实验是对五种不同种子类型的菜籽，粗米，高粱，大豆和小麦进行的。
Seed phenotyping is the idea of analyzing the morphometric characteristics of a seed to predict the behavior of the seed in terms of development, tolerance and yield in various environmental conditions. The focus of the work is the application and feasibility analysis of the state-of-the-art object detection and localization neural networks, Mask R-CNN and YOLO (You Only Look Once), for seed phenotyping using Tensorflow. One of the major bottlenecks of such an endeavor is the need for large amounts of training data. While the capture of a multitude of seed images is taunting, the images are also required to be annotated to indicate the boundaries of the seeds on the image and converted to data formats that the neural networks are able to consume. Although tools to manually perform the task of annotation are available for free, the amount of time required is enormous. In order to tackle such a scenario, the idea of domain randomization i.e. the technique of applying models trained on images containing simulated objects to real-world objects, is considered. In addition, transfer learning i.e. the idea of applying the knowledge obtained while solving a problem to a different problem, is used. The networks are trained on pre-trained weights from the popular ImageNet and COCO data sets. As part of the work, experiments with different parameters are conducted on five different seed types namely, canola, rough rice, sorghum, soy, and wheat.
