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Open AccessArticle

Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot

1
Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore
2
Layorz Private Limited, Tamil Nadu, Karur 639117, India
*
Author to whom correspondence should be addressed.
Academic Editor: Anastasios Doulamis
Sensors 2021, 21(8), 2595; https://doi.org/10.3390/s21082595
Received: 16 February 2021 / Revised: 30 March 2021 / Accepted: 1 April 2021 / Published: 7 April 2021
(This article belongs to the Section Sensors and Robotics)
The pavement inspection task, which mainly includes crack and garbage detection, is essential and carried out frequently. The human-based or dedicated system approach for inspection can be easily carried out by integrating with the pavement sweeping machines. This work proposes a deep learning-based pavement inspection framework for self-reconfigurable robot named Panthera. Semantic segmentation framework SegNet was adopted to segment the pavement region from other objects. Deep Convolutional Neural Network (DCNN) based object detection is used to detect and localize pavement defects and garbage. Furthermore, Mobile Mapping System (MMS) was adopted for the geotagging of the defects. The proposed system was implemented and tested with the Panthera robot having NVIDIA GPU cards. The experimental results showed that the proposed technique identifies the pavement defects and litters or garbage detection with high accuracy. The experimental results on the crack and garbage detection are presented. It is found that the proposed technique is suitable for deployment in real-time for garbage detection and, eventually, sweeping or cleaning tasks. View Full-Text
Keywords: pavement cracks detection; garbage detection; machine learning; self-reconfigurable; pavement sweeping robot pavement cracks detection; garbage detection; machine learning; self-reconfigurable; pavement sweeping robot
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MDPI and ACS Style

Ramalingam, B.; Hayat, A.A.; Elara, M.R.; Félix Gómez, B.; Yi, L.; Pathmakumar, T.; Rayguru, M.M.; Subramanian, S. Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot. Sensors 2021, 21, 2595. https://doi.org/10.3390/s21082595

AMA Style

Ramalingam B, Hayat AA, Elara MR, Félix Gómez B, Yi L, Pathmakumar T, Rayguru MM, Subramanian S. Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot. Sensors. 2021; 21(8):2595. https://doi.org/10.3390/s21082595

Chicago/Turabian Style

Ramalingam, Balakrishnan; Hayat, Abdullah A.; Elara, Mohan R.; Félix Gómez, Braulio; Yi, Lim; Pathmakumar, Thejus; Rayguru, Madan M.; Subramanian, Selvasundari. 2021. "Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot" Sensors 21, no. 8: 2595. https://doi.org/10.3390/s21082595

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