TY - GEN
T1 - Application of the Deep Learning Methodology for the Detection of Cracks in Asphalt Roads
AU - Neyra, Luis Antonio Elespuru
AU - Tolentino, Marco Antonio Llacza
AU - Lizano, Aldo Rafael Bravo
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Insufficient data availability and suboptimal monitoring systems notably reduced the lifespan of flexible pavements. This study addressed these challenges by introducing an innovative tool to enhance control over pavement conditions. Initial field observations identified various types of cracking, forming the basis for a comprehensive photogrammetric data survey. This dataset was then employed to train a Deep Learning model for object detection. The results showcased the model’s exceptional reliability in identifying pavement cracks, achieving an impressive accuracy rate of 83.33%. The study emphasizes the practical viability of the proposed tool as an effective means of monitoring roadway conditions. By overcoming data limitations and monitoring deficiencies, this research not only contributes to the progression of pavement maintenance practices but also establishes a solid foundation for creating a maintenance and repair priority map. This serves as a valuable tool for targeting interventions, enhancing the longevity and overall performance of flexible pavements, and represents a significant advancement in sustainable infrastructure management.
AB - Insufficient data availability and suboptimal monitoring systems notably reduced the lifespan of flexible pavements. This study addressed these challenges by introducing an innovative tool to enhance control over pavement conditions. Initial field observations identified various types of cracking, forming the basis for a comprehensive photogrammetric data survey. This dataset was then employed to train a Deep Learning model for object detection. The results showcased the model’s exceptional reliability in identifying pavement cracks, achieving an impressive accuracy rate of 83.33%. The study emphasizes the practical viability of the proposed tool as an effective means of monitoring roadway conditions. By overcoming data limitations and monitoring deficiencies, this research not only contributes to the progression of pavement maintenance practices but also establishes a solid foundation for creating a maintenance and repair priority map. This serves as a valuable tool for targeting interventions, enhancing the longevity and overall performance of flexible pavements, and represents a significant advancement in sustainable infrastructure management.
KW - Asphalt pavement
KW - Crack detection
KW - Crack map
KW - Deep Learning
KW - Monitoring
UR - https://www.scopus.com/pages/publications/85202608754
U2 - 10.1007/978-3-031-66961-3_18
DO - 10.1007/978-3-031-66961-3_18
M3 - Contribución a la conferencia
AN - SCOPUS:85202608754
SN - 9783031669606
T3 - Smart Innovation, Systems and Technologies
SP - 195
EP - 205
BT - Proceedings of the 9th Brazilian Technology Symposium (BTSym’23) - Emerging Trends and Challenges in Technology
A2 - Iano, Yuzo
A2 - Arthur, Rangel
A2 - Saotome, Osamu
A2 - Kemper Vásquez, Guillermo Leopoldo
A2 - de Moraes Gomes Rosa, Maria Thereza
A2 - Gomes de Oliveira, Gabriel
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th Brazilian Technology Symposium on Emerging Trends and Challenges in Technology, BTSym 2023
Y2 - 24 October 2023 through 26 October 2023
ER -