Yun Chaeyeong, Kim Yu Hwan, Lee Sung Jae, Im Su Jin, Park Kang Ryoung
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea.
Plant Phenomics. 2023 Apr 5;5:0031. doi: 10.34133/plantphenomics.0031. eCollection 2023.
Automatically segmenting crops and weeds in the image input from cameras accurately is essential in various agricultural technology fields, such as herbicide spraying by farming robots based on crop and weed segmentation information. However, crop and weed images taken with a camera have motion blur due to various causes (e.g., vibration or shaking of a camera on farming robots, shaking of crops and weeds), which reduces the accuracy of crop and weed segmentation. Therefore, robust crop and weed segmentation for motion-blurred images is essential. However, previous crop and weed segmentation studies were performed without considering motion-blurred images. To solve this problem, this study proposed a new motion-blur image restoration method based on a wide receptive field attention network (WRA-Net), based on which we investigated improving crop and weed segmentation accuracy in motion-blurred images. WRA-Net comprises a main block called a lite wide receptive field attention residual block, which comprises modified depthwise separable convolutional blocks, an attention gate, and a learnable skip connection. We conducted experiments using the proposed method with 3 open databases: BoniRob, crop/weed field image, and rice seedling and weed datasets. According to the results, the crop and weed segmentation accuracy based on mean intersection over union was 0.7444, 0.7741, and 0.7149, respectively, demonstrating that this method outperformed the state-of-the-art methods.
在诸如基于作物和杂草分割信息的农用机器人喷洒除草剂等各种农业技术领域中，准确自动分割来自相机的图像中的作物和杂草至关重要。然而，用相机拍摄的作物和杂草图像由于各种原因（例如，农用机器人上相机的振动或晃动、作物和杂草的晃动）会出现运动模糊，这降低了作物和杂草分割的准确性。因此，针对运动模糊图像进行鲁棒的作物和杂草分割至关重要。然而，以往的作物和杂草分割研究并未考虑运动模糊图像。为了解决这个问题，本研究提出了一种基于宽感受野注意力网络（WRA-Net）的新的运动模糊图像恢复方法，并在此基础上研究提高运动模糊图像中作物和杂草的分割精度。WRA-Net包括一个称为轻量级宽感受野注意力残差块的主块，该主块包括改进的深度可分离卷积块、注意力门和可学习的跳跃连接。我们使用所提出的方法对3个开放数据库进行了实验：BoniRob、作物/杂草田间图像以及水稻幼苗和杂草数据集。根据结果，基于平均交并比的作物和杂草分割精度分别为0.7444、0.7741和0.7149，表明该方法优于现有方法。
