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This is a Systematic Review study (also classified as: Literature Review).

A Comprehensive Investigation on Convolutional Neural Network-Based Product Defect Detection in Industrial Applications

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Review reveals diverse convolutional neural network models enhance product defect detection, indicating challenges remain.

CNN-based product defect detection improves accuracy, efficiency, and robustness in industrial applications.
Models like MobileNet and ResNet enhance performance while attention mechanisms help localize subtle defects.
Current review categorizes methods into baseline, attention-based, and hybrid models, highlighting their strengths.
Challenges such as model interpretability and deployment on edge devices must be addressed for practical use.

Shaoqin Chen (2025) studied this question.

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View Full Paper

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Authors

Review reveals diverse convolutional neural network models enhance product defect detection, indicating challenges remain.

CNN-based product defect detection improves accuracy, efficiency, and robustness in industrial applications.
Models like MobileNet and ResNet enhance performance while attention mechanisms help localize subtle defects.
Current review categorizes methods into baseline, attention-based, and hybrid models, highlighting their strengths.
Challenges such as model interpretability and deployment on edge devices must be addressed for practical use.

Shaoqin Chen (2025) studied this question.

View Full Paper

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