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- Title
Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot.
- Authors
Ramalingam, Balakrishnan; Hayat, Abdullah Aamir; Elara, Mohan Rajesh; Félix Gómez, Braulio; Yi, Lim; Pathmakumar, Thejus; Rayguru, Madan Mohan; Subramanian, Selvasundari; Doulamis, Anastasios
- Abstract
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.
- Subjects
NVIDIA Corp.; DEEP learning; PAVEMENTS; PAVEMENT management; CONVOLUTIONAL neural networks; GRAPHICS processing units; ROBOTS
- Publication
Sensors (14248220), 2021, Vol 21, Issue 8, p2595
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s21082595