School of Electrical and Computer Engineering, University of Georgia, Athens, GA 30602, USA.
Department of Mechanical Engineering, Clemson University, Clemson, SC 29634, USA.
Sensors (Basel). 2022 Dec 28;23(1):309. doi: 10.3390/s23010309.
This paper develops an approach to perform binary semantic segmentation on root images for plant root phenotyping using a conditional generative adversarial network (cGAN) to address pixel-wise class imbalance. Specifically, we use Pix2PixHD, an image-to-image translation cGAN, to generate realistic and high resolution images of plant roots and annotations similar to the original dataset. Furthermore, we use our trained cGAN to triple the size of our original root dataset to reduce pixel-wise class imbalance. We then feed both the original and generated datasets into SegNet to semantically segment the root pixels from the background. Furthermore, we postprocess our segmentation results to close small, apparent gaps along the main and lateral roots. Lastly, we present a comparison of our binary semantic segmentation approach with the state-of-the-art in root segmentation. Our efforts demonstrate that cGAN can produce realistic and high resolution root images, reduce pixel-wise class imbalance, and our segmentation model yields high testing accuracy (of over 99%), low cross entropy error (of less than 2%), high Dice Score (of near 0.80), and low inference time for near real-time processing.
本文提出了一种使用条件生成对抗网络（cGAN）对根图像进行二进制语义分割的方法，用于植物根系表型分析，以解决像素级类不平衡问题。具体来说，我们使用 Pix2PixHD，一种图像到图像的翻译 cGAN，生成与原始数据集相似的逼真的、高分辨率的植物根系图像和注释。此外，我们使用训练好的 cGAN 将原始根数据集的大小扩大三倍，以减少像素级类不平衡。然后，我们将原始数据集和生成的数据集同时输入到 SegNet 中，对根像素和背景进行语义分割。此外，我们对分割结果进行后处理，以封闭主根和侧根上的小而明显的缝隙。最后，我们将我们的二进制语义分割方法与根分割的最新技术进行了比较。我们的努力表明，cGAN 可以生成逼真的、高分辨率的根图像，减少像素级类不平衡，并且我们的分割模型在测试时表现出了很高的准确性（超过 99%），交叉熵误差低（低于 2%），Dice 分数高（接近 0.80），推断时间短，适用于近实时处理。
