Abstract：Aiming at the problem of low magnification defect rating of continuous casting billets, a system solution based on deep learning framework was established. Based on the defect target detection algorithm of YOLO V4, the detection and recognition of defects of detection class are carried out. The standard Average Precision (AP) index is used as the evaluation index. The AP of “central pipe”, “central porosity”, “nonmetallic inclusion”, “subsurface blowhole” and “central segregation” reached 82.19%, 97.63%, 54.27%, 66.20% and 29.29% respectively. The defect instance segmentation algorithm based on MASK RCNN was used to detect and identify segmented defects. Taking the standard AP(0.5-0.95) as the evaluation index, the AP(0.5-0.95) for detecting and segmentation of four types of defects, namely, “central crack”, “corner crack”, “middle crack” and “subcutaneous crack”, reached 0.78. In particular, From the perspective of production and application, AP(0.5) reaches 0.96, which can better meet the needs of defect detection.
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