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HMSF Rope Condition Classification
Introduction
This project aimed to develop an image classification system that categorizes HMSF rope conditions into three categories:
GOOD
Comprehensive analysis and classification of rope conditions for good category.
SATISFACTORY
Comprehensive analysis and classification of rope conditions for satisfactory category.
DISCARD
Comprehensive analysis and classification of rope conditions for discard category.
Data Collection and Dataset Construction
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External Abrasion
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Internal Abrasion
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Pulled Strands
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Cut Rope
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Chemical Degradation
Model Selection and Training
Why YOLO?
  • Supports classification tasks
  • Offers speed, simplicity, and real-time performance
  • Ultralytics framework provides built-in training
YOLOv8s: The Lightweight Variant
  • Small size → faster training
  • Lower resource requirements
  • Reduced overfitting risk
Limitations
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Data Scarcity
Very little real-world data. Web scraping yielded too few usable images.
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Synthetic Image Quality
Generated images visually similar → dataset bias
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Overfitting Risks
Lack of diversity in synthetic images caused overfitting
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Misclassification Potential
Real-world issues (lighting, angles, noise) can reduce prediction confidence
Conclusion
This project successfully demonstrates a working pipeline for classifying HMSF ropes using deep learning:
  • Creative data generation methods
  • Efficient training with YOLOv8s
  • High classification accuracy achieved
Prismatic Softwares Pvt. Ltd

Swaroop Complex, Third Floor C.T.S. No.41, 14-15, Karve Rd, Erandwane, Pune, Maharashtra 411004