Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Dual-Metric Neural Network with Attention Guidance for Surface Defect Few-Shot Detection in Smart Manufacturing
AU - Gao, Pengjie
AU - Wang, Junliang
AU - Xia, Min
AU - Qin, Zijin
AU - Zhang, Jie
PY - 2023/9/7
Y1 - 2023/9/7
N2 - As an important application of human-robot collaboration, intelligent detection of surface defects is crucial for production quality control, which also helps in relieving the workload of technical staff in human-centric smart manufacturing. To accurately detect defects with limited samples in industrial practice, a dual-metric neural network with attention guided is proposed. First, an attention-guided recognition network with channel attention and position attention module is designed to efficiently learn representative defect features with limited samples. Second, aiming to detect defects with confusing surface images, a dual-metric function is presented to learn the classification boundary by controlling the distance of samples in feature space from intra-class and inter-class. The experiment results on the fabric defect dataset demonstrate that the proposed approach outperforms state-of-the-art methods in accuracy, recall, precision, F1-score, and few-shot accuracy. Further comparative experiments reveal that the dual-metric function is superior in improving the few-shot detection accuracy for the defect patterns of fabric.
AB - As an important application of human-robot collaboration, intelligent detection of surface defects is crucial for production quality control, which also helps in relieving the workload of technical staff in human-centric smart manufacturing. To accurately detect defects with limited samples in industrial practice, a dual-metric neural network with attention guided is proposed. First, an attention-guided recognition network with channel attention and position attention module is designed to efficiently learn representative defect features with limited samples. Second, aiming to detect defects with confusing surface images, a dual-metric function is presented to learn the classification boundary by controlling the distance of samples in feature space from intra-class and inter-class. The experiment results on the fabric defect dataset demonstrate that the proposed approach outperforms state-of-the-art methods in accuracy, recall, precision, F1-score, and few-shot accuracy. Further comparative experiments reveal that the dual-metric function is superior in improving the few-shot detection accuracy for the defect patterns of fabric.
KW - Industrial and Manufacturing Engineering
KW - Computer Science Applications
KW - Mechanical Engineering
KW - Control and Systems Engineering
U2 - 10.1115/1.4063356
DO - 10.1115/1.4063356
M3 - Journal article
SP - 1
EP - 31
JO - Journal of Manufacturing Science and Engineering
JF - Journal of Manufacturing Science and Engineering
SN - 1087-1357
ER -