Robotics & Machine Learning Daily News2024,Issue(Jun.6) :26-26.

Studies from Kocaeli University in the Area of Artificial Intelligence Published (AdvancingTire Safety: Explainable Artificial Intelligence- Powered Foreign Obje ct Defect Detection with Xception Networks and Grad-CAM Interpretation)

科贾利大学在人工智能领域的研究发表(AdvancingTire Safety:可解释的人工智能驱动的国外Obje CT缺陷检测与Xception网络和Grad-CAM解释)

Robotics & Machine Learning Daily News2024,Issue(Jun.6) :26-26.

Studies from Kocaeli University in the Area of Artificial Intelligence Published (AdvancingTire Safety: Explainable Artificial Intelligence- Powered Foreign Obje ct Defect Detection with Xception Networks and Grad-CAM Interpretation)

科贾利大学在人工智能领域的研究发表(AdvancingTire Safety:可解释的人工智能驱动的国外Obje CT缺陷检测与Xception网络和Grad-CAM解释)

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摘要

由机器人与机器学习每日新闻的新闻记者兼工作人员新闻编辑-人工智能的新数据在一份新的报告中呈现。根据NewsRx编辑在科贾利大学的新闻报道,研究表明,"轮胎缺陷的自动检测已经成为轮胎生产公司的一个重要问题,因为这些缺陷可能会导致道路事故和生命损失"。这项研究的资助者包括土耳其科学和技术研究中心。新闻记者从科贾利大学的研究中得到一句话:“用肉眼无法检测到轮胎内部结构的缺陷,因此,使用X射线照相机采集轮胎的射线照相图像,然后由质量控制操作员检查该图像。”本文提出了一种基于Xception和grad-cam方法的可解释的深度学习模型,在由2303个缺陷轮胎和49198个缺陷轮胎组成的真实轮胎数据集上对模型进行了微调和训练。使用自定义增强技术对缺陷轮胎类别进行增强,以解决数据集的不平衡问题。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Kocaeli Univer sity by NewsRx editors, research stated, “Automatic detection of tire defects ha s become an important issue for tire production companies since these defects ca use road accidents and loss of human lives.” Funders for this research include The Scientific And Technological Research Coun cil of Turkiye. The news reporters obtained a quote from the research from Kocaeli University: “ Defects in the inner structure of the tire cannot be detected with the naked eye ; thus, a radiographic image of the tire is gathered using X-ray cameras. This i mage is then examined by a quality control operator, and a decision is made on w hether it is a defective tire or not. Among all defect types, the foreign object type is the most common and may occur anywhere in the tire. This study proposes an explainable deep learning model based on Xception and Grad-CAM approaches. T his model was fine-tuned and trained on a novel real tire dataset consisting of 2303 defective tires and 49,198 non-defective. The defective tire class was augm ented using a custom augmentation technique to solve the imbalance problem of th e dataset.”

Key words

Kocaeli University/Artificial Intellige nce/Emerging Technologies/Machine Learning

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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