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ROAD: Robotics-Assisted Onsite Data Collection and Deep Learning Enabled Robotic Vision System for Identification of Cracks on Diverse Surfaces

Author

Listed:
  • Renu Popli

    (Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140601, Punjab, India)

  • Isha Kansal

    (Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140601, Punjab, India)

  • Jyoti Verma

    (Department of Computer Science and Engineering, Punjabi University, Patiala 147002, Punjab, India)

  • Vikas Khullar

    (Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140601, Punjab, India)

  • Rajeev Kumar

    (Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140601, Punjab, India)

  • Ashutosh Sharma

    (Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140601, Punjab, India
    Department of Informatics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India)

Abstract

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.

Suggested Citation

  • Renu Popli & Isha Kansal & Jyoti Verma & Vikas Khullar & Rajeev Kumar & Ashutosh Sharma, 2023. "ROAD: Robotics-Assisted Onsite Data Collection and Deep Learning Enabled Robotic Vision System for Identification of Cracks on Diverse Surfaces," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9314-:d:1167135
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    References listed on IDEAS

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    1. Vaughn Peter Golding & Zahra Gharineiat & Hafiz Suliman Munawar & Fahim Ullah, 2022. "Crack Detection in Concrete Structures Using Deep Learning," Sustainability, MDPI, vol. 14(13), pages 1-25, July.
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