Mobile phone screen defect detection based on improved YOLOv8n network

Abstract

The continuous development of science and technology has led to mobile phones becoming an indispensable part of people’s lives. The screens of used mobile phones have significant recycling value. However, the three most prevalent types of defects affecting screens—oil, scratches and stains—have a significant impact on the efficacy of their recovery. In light of the suboptimal precision of conventional defect detection techniques, this paper proposes a mobile phone screen defect detection method based on the enhanced YOLOv8n network. Additionally, a mobile phone screen defect detection device has been devised to facilitate the detection process. The enhanced model incorporates the CBAM attention mechanism within the SPPF module with a view to enhancing the multi-scale feature fusion capability of the model. In addition, a small target detection layer has been added with a view to enhancing the detection accuracy of defects that are small in size and contrast. Furthermore, the BiFPN module is integrated to enhance the performance of the feature pyramid network, facilitate the capture of multi-scale information. Finally, the issue of the lack of dynamic focusing of the CIoU loss function is addressed by replacing it with the WIoU. The experimental findings demonstrate that, in comparison with YOLOv8n, YOLOv8n-SDBW exhibits a mAP@50 enhancement of 0.83% and a mAP@50:95 improvement of 2.09%, while maintaining a minimal increase in model parameters and complexity.