Improved bearing surface defect detection method based on YOLOv8
Aiming at the problems of high missing rate and high model complexity of deep learning model in the process of bearing surface defect detection,an improved defect detection algorithm based on YOLOv8 was proposed.Firstly,the GSConv lightweight convolutional module is introduced into the backbone network,and the GSConv module is used to replace the ordinary convolutional module,which reduces the calculation amount of the model without affecting the accuracy of the model.Secondly,CBAM convolutional attention module is introduced to improve the detection accuracy by improving the network feature extraction technology.Experimental results show that the accuracy of the improved model on the self-built bearing surface defect de-tection dataset is 92.6%,which is 3.8%higher than that of the original model(88.8%).While the accuracy is improved,the computational cost is also reduced from 8.2GFLOPs to 8.0 GFLOPs,which proves the effec-tiveness of the improved model for the detection of bearing defects.
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