A Survey of Incremental Deep Learning for Defect Detection in Manufacturing

Reenu Mohandas, Mark Southern, Eoin O’Connell, Martin J. Hayes

Big Data and Cognitive Computing 2024 article cited by 20

Source Open-access PDF More in Engineering & Materials

importance 4/10 ai assisted Industrial Vision Systems and Defect Detection manufacturing quality control stage: research

Problem

Deep learning defect detection models in manufacturing need to be rigorously updated in real time with new tasks, classes or decision pathways without degrading, e.g. via catastrophic forgetting.

Why it matters

Defect detection accuracy affects product quality, processing time and throughput in manufacturing.

What the AI did

The paper surveys incremental (continual) deep learning methods that incorporate new process, training or validation information into inspection models while mitigating catastrophic forgetting.

Results

not stated in source

Impact

The survey notes deep learning has improved defect-detection accuracy and throughput, but presents no new measured result.

Record

Venue	Big Data and Cognitive Computing
Published	2024
Work type	article
Authors	Reenu Mohandas, Mark Southern, Eoin O’Connell, Martin J. Hayes
Citations	20 (OpenAlex, at import time)
References	125
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.3390/bdcc8010007
Open access	gold
Keywords	incremental learning, defect detection, manufacturing, catastrophic forgetting, deep learning

Importance rating

4 / 10

real but narrower advance

Survey of continual learning for manufacturing defect detection; abstract claims improved accuracy/throughput across use cases but gives no scale or new results.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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