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As the core component of a phone, the quality of screen is directly related to the user's experience. Therefore, mobile phone screen defect detection has become an important part of industrial production. However, the surface defect detection of mobile LCD screens still faces problems such as low detection accuracy and a large number of model parameters, which cannot meet the actual industrial production needs. After studing existing defect detection algorithms and classical semantic segmentation models, an improved mobile phone LCD screen defect detection model based on PSPNet is proposed to solve the problems. MobileNetV3 is used to replace the original ResNet50 as the backbone, which effectively reduces the model parameters and shortens the training time. A multi-scale pyramid pooling module is proposed to effectively integrate multi-scale contextual information, which improves the feature extraction ability of the model. It also effectively addresses the issues of small defect sizes, blurred boundaries, and significant differences in the size of the same defect in screen images Meanwhile, the introduction of attention mechanism improves the anti-interference ability of the model. The experimental results show that the accuracy of the improved model on the mobile phone LCD screen dataset has significantly better accuracy than other traditional semantic segmentation models.