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Ensemble-Based Road Surface Crack Detection: A Comprehensive Approach

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Big Data and Artificial Intelligence (BDA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14418))

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Abstract

The existence of road surface cracks erodes the structural robustness of the infrastructure and casts shadows of risks for countless motorists and walkers. The timely and efficient detection of road cracks is of utmost importance for maintenance and mitigating further deterioration. Currently, existing techniques to identify cracks entail physical examinations rather than deploying automated image-based techniques, which leads to costly and labor-intensive operations. Incorporating automated crack detection systems is necessary to optimize processes, reduce costs, and enable proactive maintenance efforts to enhance road safety and durability. This paper presents a comprehensive study on road crack detection, aiming to develop an accurate and efficient system to identify cracks on road surfaces. The proposed approach employs a two-phase Convolutional Neural Network (CNN) in conjunction with the Extreme Learning Machine (ELM) by harnessing advanced deep learning techniques. The model showcases outstanding performance in classifying road cracks, as evidenced by thorough experimentation and evaluation on a well-known CCIC dataset. The proposed approach contributes to the advancement of preventive maintenance strategies and the augmentation of road safety measures. The findings highlight the potential of the combined Conv-ELM approach to automate road crack detection, paving the way for improved infrastructure management and streamlined maintenance practices. This research marks a significant advancement in fostering dependable and resilient transportation infrastructures.

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Notes

  1. 1.

    https://www.kaggle.com/datasets/arnavr10880/concrete-crack-images-for-classification.

  2. 2.

    https://keras.io/api/layers/activations/#relu-function.

  3. 3.

    https://mathworld.wolfram.com/Moore-PenroseMatrixInverse.html.

  4. 4.

    https://keras.io/api/layers/activations/#softmax-function.

  5. 5.

    https://keras.io/api/layers/activations/#tanh-function.

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Author information

Authors and Affiliations

  1. Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Rajendra Kumar Roul &  Navpreet

  2. Department of Mathematics, BITS Pilani, K.K.Birla Goa Campus, Sancoale, India

    Jajati Keshari Sahoo

Authors
  1. Rajendra Kumar Roul
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  2. Navpreet
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  3. Jajati Keshari Sahoo
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Corresponding author

Correspondence to Rajendra Kumar Roul .

Editor information

Editors and Affiliations

  1. Indraprastha Institute of Information Technology, Delhi, India

    Vikram Goyal

  2. University of Delhi, Delhi, India

    Naveen Kumar

  3. Nanyang Technological University, Singapore, Singapore

    Sourav S. Bhowmick

  4. Indian Institute of Technology, Kharagpur, India

    Pawan Goyal

  5. Birla Institute of Technology and Science, Pilani, India

    Navneet Goyal

  6. Indraprastha Institute of Information Technology, Delhi, India

    Dhruv Kumar

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Roul, R.K., Navpreet, Sahoo, J.K. (2023). Ensemble-Based Road Surface Crack Detection: A Comprehensive Approach. In: Goyal, V., Kumar, N., Bhowmick, S.S., Goyal, P., Goyal, N., Kumar, D. (eds) Big Data and Artificial Intelligence. BDA 2023. Lecture Notes in Computer Science, vol 14418. Springer, Cham. https://doi.org/10.1007/978-3-031-49601-1_12

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  • DOI: https://doi.org/10.1007/978-3-031-49601-1_12

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