ArticleSensors (Basel, Switzerland)2021
Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Road Condition Monitoring Using Smart Sensing and Artificial Intelligence: A Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Self-moving multi-sensor AI-based robotic technology for road crack inspection.Frontiers in robotics and AI · 2026Article
- An integrated tiny-YOLO v3 and Q-iteration framework for stable, energy-efficient autonomous navigation of quadruped robots on AMB82-mini microcontrollers.Scientific reports · 2025Article
- Optimal selective floor cleaning using deep learning algorithms and reconfigurable robot hTetro.Scientific reports · 2022Article
- Human behavior in image-based Road Health Inspection Systems despite the emerging AutoML.Journal of big data · 2022Article
- Autonomous detection and sorting of litter using deep learning and soft robotic grippers.Frontiers in robotics and AI · 2022Article
- Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor.Scientific reports · 2021Article
- Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework.Sensors (Basel, Switzerland) · 2021Article
- sTetro-Deep Learning Powered Staircase Cleaning and Maintenance Reconfigurable Robot.Sensors (Basel, Switzerland) · 2021Article
- AI Enabled IoRT Framework for Rodent Activity Monitoring in a False Ceiling Environment.Sensors (Basel, Switzerland) · 2021Article
- An Autonomous Robot-Aided Auditing Scheme for Floor Cleaning.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.