Abstract: Various defects occur in the manufacturing process of magnetic tiledue to process problems,and traditional detection algorithms have slow detection speed and low accuracy.In order to achieve fast and effective detection of surface defects of magnetic tiles,this paper proposes a defect detection method for magnetic tiles with improved YOLOv4 algorithm.Firstly,the scSE attention module is embedded in the residual unit of CSPnet in the feature extraction backbone network to enhance the spatial features and channel features of small targets.Secondly,the empty convolutional space pooling pyramid(ASPP) module is used instead of the original SPP module to increase the perceptual field of convolutional kernel,retain more image details and enhance information relevance.Finally,the traditional convolution in the five convolution blocks is replaced by the depth-separable convolution in the neck part to better extract the feature information and reduce the number of parameters of the model.Experimental results show that the improved YOLOv4 algorithm achieves an average accuracy value of 96.67%,a detection speed of 44 ms,and a model size of 249 MB.It is significantly better than the original algorithm and has higher detection accuracy and practicality.

Key words: Defect detection, YOLOv4, scSE attention, Void convolution pooling, Depth-separabl