To address the issues that the detection rapidity and accuracy are disturbed by the tiny defects,different scales and other factors on the surface white print of the mobile phone chip shielding shell,an long short link and double attention network (LSDANet)-based surface defect detection method is devised in this paper.First,the feature extraction ability of the network model for defects with different scales is enhanced via constructing an encoding and decoding-based semantic segmentation model and utilizing the long short-distance connection path.Second,the feature weights of white print defects with a size of 5 to 10 pixel in space and channel are increased via designing the space-and channel-based attention mechanisms,respectively.Ultimately,a LSDANet defect detection network using the dual attention mechanism and long short-distance connection path segmentation model is proposed for surface defect detection of the mobile phone chip shielding shell.The experimental results demonstrate that the detection performances of the LSDANet-based algorithm in mean pixel accuracy,mean intersection over union and frames per second are 96.21%,66.13% and 39.03,which are superior to the other semantic segmentation methods in terms of detection precision and speed.