Dataset Information

Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network.

ABSTRACT: 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 ?seg and ?main, 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.

SUBMITTER: Huang J

PROVIDER: S-EPMC7875430 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network.

Huang Jian J Guixiong Liu L He Binyuan B

PloS one 20210210 2

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) ...[more]

PMID: 33566844

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Project description:There have been few anatomical structure segmentation studies using deep learning. Numbers of training and ground truth images applied were small and the accuracies of which were low or inconsistent. For a surgical video anatomy analysis, various obstacles, including a variable fast-changing view, large deformations, occlusions, low illumination, and inadequate focus occur. In addition, it is difficult and costly to obtain a large and accurate dataset on operational video anatomical structures, including arteries. In this study, we investigated cerebral artery segmentation using an automatic ground-truth generation method. Indocyanine green (ICG) fluorescence intraoperative cerebral videoangiography was used to create a ground-truth dataset mainly for cerebral arteries and partly for cerebral blood vessels, including veins. Four different neural network models were trained using the dataset and compared. Before augmentation, 35,975 training images and 11,266 validation images were used. After augmentation, 260,499 training and 90,129 validation images were used. A Dice score of 79% for cerebral artery segmentation was achieved using the DeepLabv3+ model trained using an automatically generated dataset. Strict validation in different patient groups was conducted. Arteries were also discerned from the veins using the ICG videoangiography phase. We achieved fair accuracy, which demonstrated the appropriateness of the methodology. This study proved the feasibility of operating field view of the cerebral artery segmentation using deep learning, and the effectiveness of the automatic blood vessel ground truth generation method using ICG fluorescence videoangiography. Using this method, computer vision can discern blood vessels and arteries from veins in a neurosurgical microscope field of view. Thus, this technique is essential for neurosurgical field vessel anatomy-based navigation. In addition, surgical assistance, safety, and autonomous surgery neurorobotics that can detect or manipulate cerebral vessels would require computer vision to identify blood vessels and arteries.

Dataset Information

Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network.

Publications

Fast semantic segmentation method for machine vision inspection based on a fewer-parameters atrous convolution neural network.

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