TY - JOUR
T1 - A Comprehensive Review of Convolutional Neural Networks for Defect Detection in Industrial Applications
AU - Khanam, Rahima
AU - Hussain, Muhammad
AU - Hill, Richard
AU - Allen, Paul
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2024/7/17
Y1 - 2024/7/17
N2 - Quality inspection and defect detection remain critical challenges across diverse industrial applications. Driven by advancements in Deep Learning, Convolutional Neural Networks (CNNs) have revolutionized Computer Vision, enabling breakthroughs in image analysis tasks like classification and object detection. CNNs; feature learning and classification capabilities have made industrial defect detection through Machine Vision one of their most impactful applications. This article aims to showcase practical applications of CNN models for surface defect detection across various industrial scenarios, from pallet racks to display screens. The review explores object detection methodologies and suitable hardware platforms for deploying CNN-based architectures. The growing Industry 4.0 adoption necessitates enhancing quality inspection processes. The main results demonstrate CNNs; efficacy in automating defect detection, achieving high accuracy and real-time performance across different surfaces. However, challenges like limited datasets, computational complexity, and domain-specific nuances require further research. Overall, this review acknowledges CNNs; potential as a transformative technology for industrial vision applications, with practical implications ranging from quality control enhancement to cost reductions and process optimization.
AB - Quality inspection and defect detection remain critical challenges across diverse industrial applications. Driven by advancements in Deep Learning, Convolutional Neural Networks (CNNs) have revolutionized Computer Vision, enabling breakthroughs in image analysis tasks like classification and object detection. CNNs; feature learning and classification capabilities have made industrial defect detection through Machine Vision one of their most impactful applications. This article aims to showcase practical applications of CNN models for surface defect detection across various industrial scenarios, from pallet racks to display screens. The review explores object detection methodologies and suitable hardware platforms for deploying CNN-based architectures. The growing Industry 4.0 adoption necessitates enhancing quality inspection processes. The main results demonstrate CNNs; efficacy in automating defect detection, achieving high accuracy and real-time performance across different surfaces. However, challenges like limited datasets, computational complexity, and domain-specific nuances require further research. Overall, this review acknowledges CNNs; potential as a transformative technology for industrial vision applications, with practical implications ranging from quality control enhancement to cost reductions and process optimization.
KW - Artificial intelligence
KW - Computer architecture
KW - Computer Vision
KW - Convolutional Neural Network
KW - Convolutional neural networks
KW - Deep Learning
KW - Defect detection
KW - Hardware
KW - Industrial Defect Detection
KW - Inspection
KW - Object Detection
KW - Quality Inspection: Manufacturing
KW - Reviews
KW - Computer vision
KW - convolutional neural network
KW - object detection
KW - industrial defect detection
KW - deep learning
KW - quality inspection: manufacturing
UR - http://www.scopus.com/inward/record.url?scp=85198317306&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2024.3425166
DO - 10.1109/ACCESS.2024.3425166
M3 - Article
AN - SCOPUS:85198317306
VL - 12
SP - 94250
EP - 94295
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 10589380
ER -