A light guide plate (LGP) is the main component of the backlight module of a liquid crystal display (LCD), whose defects can directly affect the display effect of LCD. To address the problems of complex texture background, low contrast, and small defect size of LGP images, this study proposes an AYOLOv5s network for defect detection of large-size LGP images. First, the LGP image is divided into different images. Then, Transformer and the attention mechanism coordinate attention are integrated in the main part and feature fusion part, and the Meta-ACON activation function is selected. Finally, massive experiments are carried out on the basis of the self-built data set LGPDD. The experimental results indicate that the defect detection algorithm for LGP enjoys the mean average accuracy (mAP) of up to 99.20% and FPS of 77, which can realize good effects in the practical detection of bright spots, scratches, foreign bodies, bumps, dirt, and other defects in the 17-inch LGP in 12 s/pcs.