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Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
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Updated: Jan 29, 2026

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect
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A Real-Time Mobile Robotic System for Crack Detection in Construction Using Two-Stage Deep Learning.

Emmanuella Ogun1, Yong Ann Voeurn2, Doyun Lee2

  • 1Mechanical Engineering Department, Georgia Southern University, Statesboro, GA 30460, USA.

Sensors (Basel, Switzerland)
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a real-time robotic system for infrastructure inspection, using deep learning for automated crack detection and autonomous navigation. The system successfully identifies micro-cracks, enhancing public safety and inspection efficiency.

Keywords:
Pix2PixROS 2SLAMU-Netautonomous inspectioncrack detectiondeep learningmobile roboticssemantic segmentationstructural health monitoring

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Area of Science:

  • Civil Engineering
  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Civil infrastructure deterioration presents significant public safety risks.
  • Manual inspections are subjective, labor-intensive, and limited by accessibility.
  • Automated inspection systems are needed to overcome these limitations.

Purpose of the Study:

  • To develop and evaluate a real-time robotic inspection system integrating deep learning and autonomous navigation.
  • To enable simultaneous automated crack detection and collision-free navigation for infrastructure assessment.
  • To improve the efficiency, accuracy, and safety of civil infrastructure inspections.

Main Methods:

  • A two-stage deep learning neural network: U-Net for segmentation and Pix2Pix conditional generative adversarial network (GAN) for refinement.
  • Adversarial residual learning to enhance boundary accuracy and reduce false positives.
  • Deployment on an Unmanned Ground Vehicle (UGV) with RGB-D camera and LiDAR for perception and navigation.

Main Results:

  • The two-stage model achieved a mean Intersection over Union (mIoU) of 73.9 ± 0.6% and an F1-score of 76.4 ± 0.3% on the CrackSeg9k dataset.
  • The robotic system successfully detected micro-cracks as small as 0.3 mm in various validation tests.
  • Demonstrated robust performance in simulation, laboratory experiments, and real-world campus hallway tests.

Conclusions:

  • The proposed robotic system offers a robust, autonomous solution for field-deployable infrastructure inspection.
  • Deep learning integration significantly enhances automated crack detection capabilities.
  • The system has the potential to revolutionize infrastructure monitoring and maintenance practices.