Evaluation of automated detection of pavement defects using YOLOv3: impact of collection techniques

Authors

  • Gabriel Tavares de Melo Freitas Instituto Federal do Ceará
  • Ernesto Ferreira Nobre Júnior Universidade Federal do Ceará
  • Aline Calheiros Espíndola Universidade Federal de Alagoas

DOI:

https://doi.org/10.58922/transportes.v32i2.2796

Keywords:

Pavement, Data collection, Deep learning

Abstract

This study involved training six neural networks with tailored configurations to automatically detect problems in pavements, utilizing the YOLOv3 framework. The acquisition of images and videos depicting pavement defects was conducted using smartphones and action cameras, leading to the organization of six distinct datasets. Every neural network was subjected to training and validation with the goal of attaining optimal accuracy in automated object detection. Implementing YOLOv3 facilitated effective defect surveys, enhancing the assessment of pavement quality, and offering valuable information for decision-making in road transport management. Upon concluding the investigation, it was determined that the framing method with the highest efficacy attained a precision rate of 98%. The results demonstrate the efficacy of YOLOv3 in accurately detecting defects, underscoring the significance of data collecting and framing methods, and adding to the current body of knowledge on automated pavement defect detection.

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Published

2024-05-21

How to Cite

Tavares de Melo Freitas, G., Ferreira Nobre Júnior, E., & Calheiros Espíndola, A. (2024). Evaluation of automated detection of pavement defects using YOLOv3: impact of collection techniques. Transportes, 32(2). https://doi.org/10.58922/transportes.v32i2.2796

Issue

Vol. 32 No. 2 (2024)

Section

Articles

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Copyright (c) 2024 Gabriel Tavares de Melo Freitas, Ernesto Ferreira Nobre Júnior, Aline Calheiros Espíndola

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