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Indian pothole detection based on CNN and anchor-based deep learning method

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Abstract

Detecting potholes is one of the important tasks for determining proper strategies in ITS (Intelligent Transportation System) service and road management system. Several efforts have been made for developing a technology which can automatically detect and recognize potholes. The main contribution of this paper lies in collecting the pothole data in different Indian traffic conditions and detecting of the same using a vision-based method by defining the performance of deep learning methods like sequential convolutional neural network (CNN), and anchor-based You only Look Once3 (YOLOV3) and analyzing the models in terms of resources and performance of detection. The experiments were conducted on both models and a conclusion was drawn to bring out the benefits of the model with 98% accuracy using CNN and 83% precision using Yolov3.

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Acknowledgements

The Research is supported by Science Engineering Research Board, under startup Research Grant Program in Engineering Science with File NO.: SERB/SRG/2019/002277 and is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, KLS Gogte Institute of Technology, Belagavi, India

    Mallikarjun Anandhalli & A. Tanuja

  2. Department of Computer Science and Engineering, KLE Technological University, Hubballi, India

    Vishwanath P. Baligar

  3. Department of Computer Science and Engineering, Smt. Kamala & Sri Venkappa M. Agadi College of Engineering and Technology, Laxmeshwar, India

    Pavana Baligar

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  1. Mallikarjun Anandhalli
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Correspondence to Mallikarjun Anandhalli.

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Anandhalli, M., Tanuja, A., Baligar, V.P. et al. Indian pothole detection based on CNN and anchor-based deep learning method. Int. j. inf. tecnol. 14, 3343–3353 (2022). https://doi.org/10.1007/s41870-022-00881-5

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