Abstract
Advancements in machine learning, computer vision, and robotics have paved the way for transformative solutions in various domains, particularly in agriculture. For example, accurate identification and segmentation of fruits from field images plays a crucial role in automating jobs such as harvesting, disease detection, and yield estimation. However, achieving robust and precise infield fruit segmentation remains a challenging task since large amounts of labeled data are required to handle variations in fruit size, shape, color, and occlusion. In this paper, we develop a few-shot semantic segmentation framework for infield fruits using transfer learning. Concretely, our work is aimed at addressing agricultural domains that lack publicly available labeled data. Motivated by similar success in urban scene parsing, we propose specialized pre-training using a public benchmark dataset for fruit transfer learning. By leveraging pre-trained neural networks, accurate semantic segmentation of fruit in the field is achieved with only a few labeled images. Furthermore, we show that models with pre-training learn to distinguish between fruit still on the trees and fruit that have fallen on the ground, and they can effectively transfer the knowledge to the target fruit dataset.
Abstract (translated)
机器学习、计算机视觉和机器人技术的发展为各个领域带来了 transformative 解决方案，尤其是在农业领域。例如，准确从田间图像中识别和分割水果在自动化诸如采摘、疾病检测和产量估计等任务中扮演着关键角色。然而，实现稳健且精确的田间水果分割仍然具有挑战性，因为需要大量标记数据来处理水果的大小、形状、颜色和遮挡的变异。在本文中，我们为田间水果使用迁移学习开发了一个几 shot semantic segmentation 框架。具体来说，我们的工作旨在解决缺乏公开可用标记数据的农业领域。受到城市场景解析的成功启发，我们提出了使用公共基准数据集进行水果转移学习的专用预训练方案。通过利用预训练的神经网络，可以在仅几张标记图片的情况下实现水果在田间的准确语义分割。此外，我们还证明了经过预训练的模型能够区分仍然在树上的水果和已经掉在地上的水果，并且可以有效地将知识传递到目标水果数据集中。
URL
https://arxiv.org/abs/2405.02556
