摘要
一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-在一份新的报告中讨论了人工智能的研究结果。根据NewsRx记者在印度奥迪沙的新闻报道，研究表明，“联合国的目标是保护、评估和保护文化遗产(CH)结构，作为可持续发展的一部分。许多CH结构的设计寿命很低，接近尾声。”新闻记者从印度理工学院(IIT)Bhubaneswar的研究中获得了一句话：“因此，必须按照保护准则对CH结构进行频繁的目视检查，以确保结构完整性。这项研究实施了定制缺陷检测。”以新德里Hauz Khas村Dadi-Poti墓葬为例,基于You Only Look Once(YOLO)V5实时目标检测算法的本地化监督深度学习模型,对定制的YOLOv5模型进行训练,自动检测变色、露砖、裂纹和剥落四种缺陷。将定制的YOLOv5模型与基于ResNet 101架构的快速区域卷积神经网络(R-CNN)进行了有效性和性能比较,并采用常规人工视觉检测方法传达了所开发的基于人工智能的模型的重要性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Artificial Intell igence are discussed in a new report. According to news reporting originating in Odisha, India, by NewsRx journalists, research stated, “The United Nations aims to preserve, evaluate, and conserve cultural heritage (CH) structures as part o f sustainable development. The design life expectancy of many CH structures is s lowly approaching its end.” The news reporters obtained a quote from the research from the Indian Institute of Technology (IIT) Bhubaneswar, “It is thus imperative to conduct frequent visu al inspections of CH structures following conservation guidelines to ensure thei r structural integrity. This study implements a custom defect detection, and loc alization supervised deep learning model based on the you only look once (YOLO) v5 real-time object detection algorithm by implementing a case study of the Dadi -Poti tombs in Hauz Khas Village, New Delhi. The custom YOLOv5 model is trained to automatically detect four defects, namely, discoloration, exposed bricks, cra cks, and spalling, and tested on a dataset comprising 10291 images. The validity and performance of the custom YOLOv5 model are compared with a ResNet 101 archi tecture-based faster region-based convolutional neural network (R-CNN), and conv entional manual visual inspection methods are used to convey the significance of the developed artificial intelligence-based model.”
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