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Journal of Intelligent Manufacturing

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05-04-2023

Real-time defect detection of TFT-LCD displays using a lightweight network architecture

Authors: Ping Chen, Mingfang Chen, Sen Wang, Yanjin Song, Yu Cui, Zhongping Chen, Yongxia Zhang, Songlin Chen, Xiang Mo

Published in: Journal of Intelligent Manufacturing | Issue 3/2024

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Abstract

The mura defects of thin film transistor-liquid crystal display (TFT-LCD) panels have low contrast and random locations, which makes it impossible for us to correctly evaluate the number and type of mura defects on the image in the field inspection. In response to the above problems, this paper proposes a lightweight YOLO-ADPAM detection method based on an attention mechanism. First, we designed a K-means-ciou++ clustering algorithm using the Complete-Intersection-Over-Union loss function to cluster the anchor box size of the display defect dataset, making the bounding box regression more accurate and stable and improving the recognition and positioning accuracy of the algorithm. Second, we design a parallel attention module, combining the advantages of the channel and spatial attention mechanisms to effectively extract helpful information from feature maps. The channel attention branch can compensate for the defect information lost by global average pooling to a certain extent, and selecting a larger convolution kernel in the spatial attention branch is beneficial to retain crucial spatial information. Third, using atrous spatial pyramid pooling and depthwise separable convolution in the Neck network can further improve the receptive field of the feature map and improve the detection accuracy of the network. The experimental results show that the mAP of our proposed YOLO-ADPAM algorithm in TFT-LCD defect detection reaches 98.20%, and the detection speed reaches 83.23 FPS, which meets the detection accuracy and real-time requirements of TFT-LCD defect detection tasks.

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Title: Real-time defect detection of TFT-LCD displays using a lightweight network architecture
Authors: Ping Chen
Mingfang Chen
Sen Wang
Yanjin Song
Yu Cui
Zhongping Chen
Yongxia Zhang
Songlin Chen
Xiang Mo
Publication date: 05-04-2023
Publisher: Springer US
Published in: Journal of Intelligent Manufacturing / Issue 3/2024
Print ISSN: 0956-5515
Electronic ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-023-02110-7

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