School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, Guangdong Province, China.
PLoS One. 2021 Feb 10;16(2):e0246093. doi: 10.1371/journal.pone.0246093. eCollection 2021.
Owing to the recent development in deep learning, machine vision has been widely used in intelligent manufacturing equipment in multiple fields, including precision-manufacturing production lines and online product-quality inspection. This study aims at online Machine Vision Inspection, focusing on the method of online semantic segmentation under complex backgrounds. First, the fewer-parameters optimization of the atrous convolution architecture is studied. Atrous spatial pyramid pooling (ASPP) and residual network (ResNet) are selected as the basic architectures of ηseg and ηmain, respectively, which indicate that the improved proportion of the participating input image feature is beneficial for improving the accuracy of feature extraction during the change of the number and dimension of feature maps. Second, this study proposes five modified ResNet residual building blocks, with the main path having a 3 × 3 convolution layer, 2 × 2 skip path, and pooling layer with ls = 2, which can improve the use of image features. Finally, the simulation experiments show that our modified structure can significantly decrease segmentation time Tseg from 719 to 296 ms (decreased by 58.8%), with only a slight decrease in the intersection-over-union from 86.7% to 86.6%. The applicability of the proposed machine vision method was verified through the segmentation recognition of the China Yuan (CNY) for the 2019 version. Compared with the conventional method, the proposed model of semantic segmentation visual detection effectively reduces the detection time while ensuring the detection accuracy and has a significant effect of fewer-parameters optimization. This slows for the possibility of neural network detection on mobile terminals.
由于深度学习的最新发展，机器视觉已广泛应用于多个领域的智能制造设备中，包括精密制造生产线和在线产品质量检查。本研究旨在进行在线机器视觉检查，重点是复杂背景下的在线语义分割方法。首先，研究了空洞卷积结构的更少参数优化。采用空洞空间金字塔池化（ASPP）和残差网络（ResNet）分别作为ηseg和ηmain 的基本架构，这表明改进参与输入图像特征的比例有利于在特征图的数量和维度变化时提高特征提取的准确性。其次，本研究提出了五个改进的 ResNet 残差构建块，主路径具有 3×3 卷积层、2×2 跳过路径和池化层 ls=2，可以提高图像特征的利用率。最后，仿真实验表明，我们的改进结构可以将分割时间 Tseg 从 719 毫秒显著减少到 296 毫秒（减少了 58.8%），而交并比仅从 86.7%略微下降到 86.6%。通过对 2019 年版人民币的分割识别验证了所提出的机器视觉方法的适用性。与传统方法相比，所提出的语义分割视觉检测模型在有效减少检测时间的同时保证了检测精度，具有显著的少参数优化效果。这为神经网络在移动终端上的检测提供了可能性。
