The light guide plate is an important part of the backlight module of liquid crystal display screen. In the production process, defects such as bright spots, scratches, crushing, and shadows are inevitable, which directly affects the display effect. Aiming at this problem, according to the arrangement characteristics of the dots and the imaging effect of the defects, this study proposes a method based on machine vision for detecting defects of light guide plates. Firstly, according to the degree of density of the dots, the image is divided into a sparse zone and a dense zone. Secondly, because the dot in sparse zone will cause great interference to the defect detection, the idea of separating the dots is proposed, and the filter is used to separate the dots and the background. Suspected defect extraction is performed separately. The Gaussian derivative filter is designed to filter the dense area. On the basis of this, the gray area morphology and image calculation methods are used to extract the suspected defects in the dense area. Further, for different areas of the light guide plate, according to the quality inspection requirements of the production line, the characteristics of the defect area, and the screening rules are used to determine the defects. Finally, a large number of on-site tests are performed on the proposed detection method on the self-developed light guide plate defect detection system. The test results show that the detection precision for bright white spots, crushing and scratching defects has a detection accuracy of over 99%, which can basically meet the requirements of industrial testing.