According to the characteristics of complex gradient, uneven brightness, subtle defects, and multiple types of car navigation light guide plate image texture background, and according to the optical characteristics of light guide plate, dot arrangement, defect imaging effect, etc., a fast defect detection method combined with lightweight and cascaded deep learning network is proposed. First, based on the characteristics of defect distribution of the light guide plate, by improving the convolutional layer connection and the down-sampling method of the feature map, a lightweight two-classification network was designed to quickly segment the solid line suspected defect area. Second, the improved ResNet network was used to construct a multi-classification network. The lightweight network and the multi-classification network were cascaded and merged, and diversified features were extracted from the segmented suspected defect regions to achieve accurate defect classification. Then, defect region could be located and recognized by predicting images which were from fixed windows sliding on the completed light guide plate. Finally, a self-built dataset of light guide plate images collected from the industrial field was used, and a large number of experiments are carried out on this basis. Experimental results show that the average accuracy of the detection algorithm for light guide plate defects detection is 98.4%, and the single detection time is 1.95 s. The accuracy and real-time performance meet the requirements of industrial detection.