基于 X 射线图像和神经网络识别内部缺陷,对梨进行分类

IF 4.2 2区 工程技术 Q2 ENGINEERING, MANUFACTURING Advances in Manufacturing Pub Date : 2024-07-10 DOI:10.1007/s40436-024-00512-1
Ning Wang, Sai-Kun Yu, Zheng-Pan Qi, Xiang-Yan Ding, Xiao Wu, Ning Hu
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引用次数: 0

摘要

为了提高销售量和利润率,必须根据梨的外部形态(包括形状、颜色和光泽)和内部缺陷对其进行分类。然而,现有的分类方法主要集中于外部质量而非内部缺陷。因此,本研究结合 X 射线无损检测和卷积神经网络,开发了一种高效、准确的分类方法,可以识别内部硬化和淤伤。首先,分析了内部缺陷(即内部硬化和瘀伤)的特征与 X 射线图像灰度特征之间的关系,以提供实验数据和论证理论基础。然后,通过降低分辨率、特征增强和梯度重建等方法对 X 光图像进行处理,以提高训练效率和分类精度。最后,对 18 层残差网络(ResNet-18)进行优化和训练,以识别内部淤血和硬化,并根据识别结果对梨进行分类。结果表明,识别淤血和硬化梨的总体准确率可达 96.67%。建议的方法也可应用于其他水果的缺陷识别和质量分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Pears classification by identifying internal defects based on X-ray images and neural networks

In order to increase the sales and profitability, it is essential to classify the pears according to the external morphology (including shape, color and luster) and internal defects that can be quantitatively detected by various approaches. However, the existing classification methods concentrate mainly on the external quality rather than the internal defects. Therefore, this investigation develops an efficient and accurate classification method that can identify the internal sclerosis and bruises by combining the X-ray non-destructive testing and the convolutional neural network. Initially, the relations between the characteristics of the internal defects, i.e., internal sclerosis and bruises, and the grayscale features of the X-ray images are analyzed to provide the experimental data and demonstrate the theoretical foundations. Then, the X-ray images are processed by resolution reduction, feature enhancement and gradient reconstruction to improve the training efficiency and classification precision. Finally, the 18-layer residual network (ResNet-18) is optimized and trained to identify the internal bruises and sclerosis and classify the pears based on the identification results. It is found that the overall accuracy can reach 96.67% for identifying the bruised and sclerotic pears. The proposed method could also be applied to other fruits for defects identification and quality classification.

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来源期刊
Advances in Manufacturing
Advances in Manufacturing Materials Science-Polymers and Plastics
CiteScore
9.10
自引率
3.80%
发文量
274
期刊介绍: As an innovative, fundamental and scientific journal, Advances in Manufacturing aims to describe the latest regional and global research results and forefront developments in advanced manufacturing field. As such, it serves as an international platform for academic exchange between experts, scholars and researchers in this field. All articles in Advances in Manufacturing are peer reviewed. Respected scholars from the fields of advanced manufacturing fields will be invited to write some comments. We also encourage and give priority to research papers that have made major breakthroughs or innovations in the fundamental theory. The targeted fields include: manufacturing automation, mechatronics and robotics, precision manufacturing and control, micro-nano-manufacturing, green manufacturing, design in manufacturing, metallic and nonmetallic materials in manufacturing, metallurgical process, etc. The forms of articles include (but not limited to): academic articles, research reports, and general reviews.
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