Abstract: A cascaded twin dense network based metallic surface defect detection algorithm SCSEG-Net is proposed to address the problems of low accuracy and difficulty in locating defects in real-time metal surface defect detection.The method incorporates atrous spatial pyramid pooling structure to obtain feature maps with different sampling rates to capture multi-scale information.Meanwhile,in order to enhance the classification accuracy,the low-level texture and boundary features obtained by shallow convolution and the complex high-level feature information obtained by deep convolution are fused during training to better optimize the training parameters through the cascade network.SCSEG-Net can convert defect images into pixel-level prediction masks and quickly acquire real defect classes.The SCSEG-Net algorithm is trained,evaluated and verified on the industry standard steel surface defect data set.The results show that compared with similar algorithms,proposed method can segment the contour of steel surface defects more accurately and complete the classification with an F1-score of 97.8%and a recall rate of 98.81%.