Advancing Crack Detection Using Deep Learning Solutions for Automated Inspection of Metallic Surfaces
importance 2/10
ai assisted
Industrial Vision Systems and Defect Detection industrial inspection stage: research
Problem
Conventional image processing struggles to detect cracks on metallic surfaces (hex nuts) under real-world lighting and shadow variation.
Why it matters
Automated crack inspection aims to supplement on-site human receiving inspections of mechanical parts.
What the AI did
A deep CNN was trained to detect cracks on metallic hex nut surfaces, avoiding explicit fault-feature comparison and tolerating changing real-world conditions.
Results
not stated in source
Impact
Evidence supports only a proof of concept; no measured performance or deployment is stated.
Record
| Venue | Indian Journal of Information Sources and Services |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Snehal Rathi, Omkar Mirajkar, Shubhangi Shukla, Laukik Deshmukh, Lokesh Dangare |
| Citations | 33 (OpenAlex, at import time) |
| References | 22 |
| Model or method | Deep Convolutional Neural Network (CNN) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.51983/ijiss-2024.14.1.4003 |
| Open access | hybrid |
| Keywords | crack detection, CNN, deep learning, metallic surface, automated inspection, image processing |
Importance rating
2
incremental / niche
Proof-of-concept crack detection on one part type (hex nuts) to supplement human receiving inspection; narrow, no stated scale.
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