人工智能
深度学习
计算机科学
机器人学
可靠性(半导体)
更安全的
过程(计算)
机器人
计算机视觉
鉴定(生物学)
人工神经网络
机器视觉
机器学习
计算机安全
量子力学
生物
植物
操作系统
物理
功率(物理)
作者
Renu Popli,Isha Kansal,Jyoti Verma,Vikas Khullar,Rajeev Kumar,Ashutosh Sharma
出处
期刊:Sustainability
[MDPI AG]
日期:2023-06-09
卷期号:15 (12): 9314-9314
被引量:1
摘要
Crack detection on roads is essential nowadays because it has a significant impact on ensuring the safety and reliability of road infrastructure. Thus, it is necessary to create more effective and precise crack detection techniques. A safer road network and a better driving experience for all road users can result from the implementation of the ROAD (Robotics-Assisted Onsite Data Collecting) system for spotting road cracks using deep learning and robots. The suggested solution makes use of a robot vision system’s capabilities to gather high-quality data about the road and incorporates deep learning methods for automatically identifying cracks. Among the tested algorithms, Xception stands out as the most accurate and predictive model, with an accuracy of over 90% during the validation process and a mean square error of only 0.03. In contrast, other deep neural networks, such as DenseNet201, InceptionResNetV2, MobileNetV2, VGG16, and VGG19, result in inferior accuracy and higher losses. Xception also achieves high accuracy and recall scores, indicating its capability to accurately identify and classify different data points. The high accuracy and superior performance of Xception make it a valuable tool for various machine learning tasks, including image classification and object recognition.
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