Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Science, Hefei, China.
University of Science and Technology of China, Hefei, China.
J Sci Food Agric. 2022 Aug 30;102(11):4854-4865. doi: 10.1002/jsfa.11848. Epub 2022 Mar 23.
Fast identification of damaged soybean seeds has undeniable importance in seed sorting and food quality. Mechanical vibration is generally used in soybean seed sorting, but this can seriously damage soybean seeds. The convolutional neural network (CNN) is considered an effective method for location and segmentation tasks. However, a CNN requires a large amount of ground truth data and has high computational cost.
First, we propose a self-supervision manner to automatically generate ground truths, which can theoretically create an almost unlimited number of labeled images. Second, instead of using popular CNNs, a novel invertible convolution (involution)-enabled scheme is proposed by using the bottleneck block of the residual networks. Third, a feature selection feature pyramid network (FS-FPN) based on involution is designed, which selects features more flexibly and adaptively. We further merge involution-based backbones and FS-FPN into a unified network, achieving an end-to-end seed location and segmentation model; the best mean average precision of location and segmentation achieved was 85.1% and 81% respectively.
The experimental results demonstrate that the proposed method greatly improves the performance of the baseline network with faster speed and fewer parameters, enabling it to detect soybean seeds more effectively. © 2022 Society of Chemical Industry.
快速识别受损大豆种子在种子分拣和食品质量方面具有不可否认的重要性。机械振动通常用于大豆种子分拣，但这会严重损坏大豆种子。卷积神经网络（CNN）被认为是定位和分割任务的有效方法。然而，CNN 需要大量的地面实况数据，并且计算成本很高。
首先，我们提出了一种自监督的方式来自动生成地面实况，理论上可以创建几乎无限数量的标记图像。其次，我们没有使用流行的 CNN，而是提出了一种新颖的基于可逆变卷积（involution）的方案，该方案使用残差网络的瓶颈块。第三，设计了基于 involution 的特征选择特征金字塔网络（FS-FPN），它更灵活和自适应地选择特征。我们进一步将基于 involution 的骨干网络和 FS-FPN 合并到一个统一的网络中，实现了一个端到端的种子定位和分割模型；定位和分割的最佳平均精度分别达到 85.1%和 81%。
实验结果表明，所提出的方法大大提高了基线网络的性能，速度更快，参数更少，能够更有效地检测大豆种子。 © 2022 化学工业协会。
