An accuracy improvement method for automatic fiber placement defect recognition based on attention data enhancement

CAO Jieqiang; ZHANG Liqiang; LI Junli; MAO Jian; LIU Gang

doi:10.12299/jsues.24-0074

Volume 39 Issue 2

Jun. 2025

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Article Navigation > Journal of Shanghai University of Engineering Science > 2025 > 39(2): 243-250

CAO Jieqiang, ZHANG Liqiang, LI Junli, MAO Jian, LIU Gang. An accuracy improvement method for automatic fiber placement defect recognition based on attention data enhancement[J]. Journal of Shanghai University of Engineering Science, 2025, 39(2): 243-250. doi: 10.12299/jsues.24-0074

Citation:

CAO Jieqiang, ZHANG Liqiang, LI Junli, MAO Jian, LIU Gang. An accuracy improvement method for automatic fiber placement defect recognition based on attention data enhancement[J]. Journal of Shanghai University of Engineering Science, 2025, 39(2): 243-250. doi: 10.12299/jsues.24-0074

Citation:

CAO Jieqiang, ZHANG Liqiang, LI Junli, MAO Jian, LIU Gang. An accuracy improvement method for automatic fiber placement defect recognition based on attention data enhancement[J]. Journal of Shanghai University of Engineering Science, 2025, 39(2): 243-250. doi: 10.12299/jsues.24-0074

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An accuracy improvement method for automatic fiber placement defect recognition based on attention data enhancement

doi: 10.12299/jsues.24-0074

School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2024-02-08
Available Online: 2025-09-30
Publish Date: 2025-06-30

Abstract

Abstract

The scarcity of infrared defect samples and the difficulty in acquiring them during the automated tape laying process result in low classification accuracy and ineffective defect identification by classification model algorithm. To address this issue, a data augmentation method based on an attention mechanism Wasserstein generative adversarial network (WGAN) was proposed. A CB-Attention module was introduced into the generator to enhance its capability to capture image feature, expand the receptive field, and improve the quality of generated images without adding extra parameters. Batch channel normalization was employed to adaptively integrate information from both channel and batch dimensions, thereby imcreasing the model's training speed and generalization ability. Experimental results demonstrate that the samples generated by the attention-based data augmentation method are diverse and of high quality. Incorporating these samples into the small dataset of automated tape laying defects significantly improves defect recongnition accuracy, which validates the ffectiveness of the proposed algorithm and lays a data foundation for automated tape laying defect classification algorithms.
- automated fiber placement defect identification,
- generative adversarial network,
- data augmentation,
- infrared image

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References(27)

References

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