| Popis: |
The surface defects of flywheel disc semi-finished products have complex and changeable morphological characteristics and random distribution. At present, relevant enterprises can only detect them through manual visual inspection. However, the low efficiency of manual inspection and the unstable inspection quality can easily lead to false inspections and missed inspections, which cannot meet the growing demand for production capacity. In order to achieve intelligent and efficient detection of defects, this paper proposes a surface defect detection algorithm for flywheel disc semi-finished products based on improved faster region-based convolutional neural networks (Faster R-CNN). First of all, based on multi-scale feature fusion, residual feature recalibration and deformable convolution, this paper designs a feature extraction network that can better capture and characterize defect morphology. Secondly, optimize the design of Faster R-CNN algorithm, use k-means++ cluster analysis to optimize the anchor generation rules in the network, so as to adapt to the defects of large aspect ratio, the region of interest (ROI) pooling calculation method incorporating global feature information is redesigned to prevent the position deviation of candidate areas when they are mapped back to the original image. Aiming at the problem that adjacent overlapping positive samples are deleted by mistake, the soft non-maximum suppression (Soft-NMS) algorithm is used to optimize the non-maximum suppression process and increase the number of positive samples output by the region proposal network(RPN). Then, the surface defect images are collected to build a data set, aiming at the problem that the data set is small and the distribution of the number of defects in each category is unbalanced, the classical data enhancement methods are used to augment the data set and equalize the defect categories. Finally, the surface defect detection and application experiment research of flywheel disc semi-finished products is carried out. The detection accuracy of the algorithm in this paper on the surface defect test set reaches 92.7%, which is 9.6% higher than the original Faster R-CNN detection accuracy, and 18.5% higher for the detection accuracy of small minor defects, and the improvement effect is more obvious. |
