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

• Published in: Journal of intelligent manufacturing Vol. 35; no. 3; pp. 1337 - 1352
• Main Authors: Chen, Ping, Chen, Mingfang, Wang, Sen, Song, Yanjin, Cui, Yu, Chen, Zhongping, Zhang, Yongxia, Chen, Songlin, Mo, Xiang
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2024; Springer Nature B.V
• Subjects: Accuracy; Advanced manufacturing technologies; Algorithms; Business and Management; Clustering; Control; Convolution; Crystal defects; Feature maps; LCDs; Liquid crystal displays; Machines; Manufacturing; Mechatronics; Object recognition; Processes; Production; Real time; Robotics; Semiconductor devices; Spatial data; Thin film transistors; Thin films; K-means-ciou; ASPP; TFT-LCD defect detection
• ISSN: 0956-5515; 1572-8145
• DOI: 10.1007/s10845-023-02110-7

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.