Hauling Rope Flaw Inspection System

I&T Solution Hauling Rope Flaw Inspection System
(REF: S-1267)
Trial Project
Solution Feature
  • Computer Vision Rope Flaws Detection (CVRFD) method is using computer vision deep learning technology.
  • CVRFD consists of three main parts, which are data collection, model training, and inference.
  • The quality of photos is crucial for CVRFD, traditional image preprocessing techniques are also used to increase image quality.
  • FPN (Feature Pyramid Network) with ResNet101 backbone is recommended
Trial Application and Expected Outcome
  • Malformation can be detected by structure and texture features.
  • Broken can be detected by the surface texture features
  • Corrosion can be detected by the brown and yellow color features.
  • Abrasion can be detected by the flat surface texture features
  • Fatigue can be detected by the structure features
Additional Solution Information Hauling_Rope_Flaw_Inspection.pdf
Info on I&T Solution Provider
Solution Provider:NCSI (HK) LIMITED
Address:21/F 1063 King's Road East, Quarry Bay, Hong Kong
Contact Person:Alan Chan
Position:CLIENT SERVICE LEAD
Tel:94512511
Email: alan.chan@hk.ncs-i.com
Webpage: https://www.ncs.co/en-sg/services/communications-engineering/

For details of the above I&T solution, please contact the I&T solution provider.