Defect Detection of Small Aluminum Casting Turbines Based on YOLOv5
Aiming at the problem of difficulty in detection of small aluminum casting turbine due to the complicated sur-face and small defect,an improved YOLOv5 surface defect detection algorithm for small aluminum casting turbine was proposed.The data enhancement strategy was utilized to balance the sample distribution of different categories for image preprocessing.The K-means++algorithm was taken to obtain the optimal prior frame suitable for the data set.The feature extraction structure of the network was modified,and CA attention mechanism module was added to the backbone network,which helps to the accurate positioning and recognition of model.Addition of a small target detec-tion layer can enhance the detection effect on small objects.The results indicate that the modified algorithm exhibits a better detection effect on small target defects,where the mean average precision(mAP)reaches 97.8%,meeting the requirement of intelligent manufacturing automation production.
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