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Mobile phone screen defect detection is an important part of mobile phone production. To achieve accurate and efficient defect detection is of great significance for improving the productivity of mobile phone industry. In the actual production process, the screen defect image features are not obvious and the defect size difference is large, which increases the difficulty of mobile phone screen defect detection. A mobile phone screen defect detection model based on PU-Faster R-CNN was proposed to solve the above problems. For the problem of obscure feature information of cell phone screen images, a multi-layer feature enhancement module was proposed to effectively enhance the target defect feature information. A multi-scale feature extraction network was constructed to effectively extract multi-scale defect feature information. In order to generate Anchor boxes with better fitting performance, an adaptive region proposal network was proposed to generate Anchor box templates with more accurate size by self-iterative clustering algorithm. The experimental results showed that the framework was superior to the mainstream mobile phone screen defect detection framework in mobile phone screen datasets.