Real-Time Defect Classification at the Edge: A Lightweight CNN Approach for Industrial-Scale Applications
Bibliographic record
Abstract
We introduce a lightweight CNN-based framework for real-time classification of surface defects and efficient deployment on industrial edge devices. The method was evaluated on the NEU-CLS dataset using several compact convolutional architectures. The GhostNet-based variant delivered the most balanced performance across accuracy, efficiency, and inference speed. For practical validation, the trained model was exported to ONNX format and deployed on a Raspberry Pi 4B, achieving stable real-time inference with hardware costs under 100 USD. The deployment workflow integrates preprocessing, streamlined inference, and result output, making it suitable for quality control scenarios. Visual outcomes and classification consistency across multiple defect types demonstrate the model's generalization and reliability. Designed for resource-constrained environments, this solution offers a deployable path toward high-precision defect recognition in embedded industrial applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".