Automatic Detection Technology for Inclusion in Al Melt Based on Machine Learning
An optimized target detection algorithm based on YOLOv5 model was proposed according to characteristics of existing slag inclusion images to reduce the negative feedback effects caused by uncertain factors such as angle and light source,improving the accuracy.According to Mosaic data enhancement,adaptive anchor frame calculation,adap-tive image scaling and other technologies,an algorithm for automatic recognition of slag inclusion images and auto-matic calculation of slag inclusion rate was designed combined with Focus and CSP structures.The results indicate that the improved YOLOv5s model can effectively enhance the target detection accuracy of sectional slag inclusion images from 83%to 97%,compared with the manually collecting method to calculate slag inclusion rate level.
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