Zhang Wenli, Chen Kaizhen, Wang Jiaqi, Shi Yun, Guo Wei
Information Department, Beijing University of Technology, Beijing, 100022, China.
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Hortic Res. 2021 Jun 1;8(1):119. doi: 10.1038/s41438-021-00553-8.
Fruit detection and counting are essential tasks for horticulture research. With computer vision technology development, fruit detection techniques based on deep learning have been widely used in modern orchards. However, most deep learning-based fruit detection models are generated based on fully supervised approaches, which means a model trained with one domain species may not be transferred to another. There is always a need to recreate and label the relevant training dataset, but such a procedure is time-consuming and labor-intensive. This paper proposed a domain adaptation method that can transfer an existing model trained from one domain to a new domain without extra manual labeling. The method includes three main steps: transform the source fruit image (with labeled information) into the target fruit image (without labeled information) through the CycleGAN network; Automatically label the target fruit image by a pseudo-label process; Improve the labeling accuracy by a pseudo-label self-learning approach. Use a labeled orange image dataset as the source domain, unlabeled apple and tomato image dataset as the target domain, the performance of the proposed method from the perspective of fruit detection has been evaluated. Without manual labeling for target domain image, the mean average precision reached 87.5% for apple detection and 76.9% for tomato detection, which shows that the proposed method can potentially fill the species gap in deep learning-based fruit detection.
水果检测与计数是园艺研究的重要任务。随着计算机视觉技术的发展，基于深度学习的水果检测技术已在现代果园中得到广泛应用。然而，大多数基于深度学习的水果检测模型是基于完全监督方法生成的，这意味着用一个领域的物种训练的模型可能无法转移到另一个领域。总是需要重新创建和标记相关的训练数据集，但这样的过程既耗时又费力。本文提出了一种域适应方法，该方法可以将从一个域训练的现有模型转移到一个新域，而无需额外的人工标记。该方法包括三个主要步骤：通过CycleGAN网络将源水果图像（带有标记信息）转换为目标水果图像（没有标记信息）；通过伪标签过程自动标记目标水果图像；通过伪标签自学习方法提高标记精度。以带标记的橙子图像数据集作为源域，未标记的苹果和番茄图像数据集作为目标域，从水果检测的角度评估了所提方法的性能。在不对目标域图像进行人工标记的情况下，苹果检测的平均精度达到87.5%，番茄检测的平均精度达到76.9%，这表明所提方法有可能填补基于深度学习的水果检测中的物种差距。
