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This is a Quantitative Study study.

October 3, 2025SensorsOpen Access

Robust Industrial Surface Defect Detection Using Statistical Feature Extraction and Capsule Network Architectures

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This analysis reveals high precision and sensitivity in surface defect detection using machine learning and deep learning techniques.

Capsule-based architectures achieved up to 100.0% precision for detecting normal class defects.
Random Forest and KNN showed high sensitivity and precision, both reaching about 99.4%.
ResNet50 attained 98.0% accuracy, indicating strong performance in analyzing surface defects.
Statistical feature extraction in combination with ML and DL techniques supports robust quality control in manufacturing.

Mjahad et al. (2025) studied this question.

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October 3, 2025SensorsOpen Access

View Full Paper

Ask AI

Bookmark

Authors

This analysis reveals high precision and sensitivity in surface defect detection using machine learning and deep learning techniques.

Capsule-based architectures achieved up to 100.0% precision for detecting normal class defects.
Random Forest and KNN showed high sensitivity and precision, both reaching about 99.4%.
ResNet50 attained 98.0% accuracy, indicating strong performance in analyzing surface defects.
Statistical feature extraction in combination with ML and DL techniques supports robust quality control in manufacturing.

Mjahad et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark