Yellow defects are inevitable due to the high production temperature of the fixture to prepare light guide plates (LGPs). This study proposes a method for detecting yellowing defects of LGP based on machine vision. Firstly, a bilateral filter is designed after gray-level transformation to reduce noise impact. Secondly, the outline of the LGPs is highlighted by the difference of neighbor pixels. Then, the contour extraction and segmentation of three LGPs are completed by the proposed self-adapting threshold filling algorithm and the line segment distance threshold. Finally, according to LGP coordinates, rectangular regions can be generated, and 81-dimensional eigenvectors and a Support Vector Machine (SVM) model can be built. A large number of experiments were carried out on the basis of the LGP images collected in the industrial field. Experimental results prove that the algorithm has high running efficiency and strong robustness and still presents high detection accuracy in the case of few training samples.