A Novel Defect Detection Method for Ferrite Shield Surface Defects by Improved Faster R-CNN

Abstract

Traditional manual inspection of small objects such as ferrite shield surface defects through naked eyes is labour-intensive and inefficient, especially existed false and missed inspection. To improve the defect detection rate and efficiency, a novel inspection method based on an improved Faster R-CNN is proposed aiming at weak feature information, small object, and diverse shapes of the defective target. This paper takes cracks, pits, impurities, and dirty defects existing on the surface of the ferrite shield as examples to verify this method. Firstly, the bidirectional feature fusion network with ResNet-50 is added to the original Faster R-CNN to obtain feature maps, which contain strong semantic information and rich location information of the defects. Then, k-means clustering with genetic algorithm is adopted for generating adaptive anchor boxes to match defects of various shapes in the region proposal network (RPN) stage. Then, the region of interest (ROI) Align instead of the ROI Pooling is introduced to eliminate the candidate frame position bias and improve the defect localization accuracy. Finally, Soft nonmaximum suppression (NMS) is used to reduce the probability of defective targets. The mean average precision (mAP) of the applied method for detecting surface defects of ferrite shields is 81.0% which is higher than the commonly used detectors such as SSD, YOLO, Retina-Net, and Cascade RCNN. The study can provide a new detection mean to obtain the detection capability and accuracy of ferrite shields and lay a solid foundation for automated detection of small objects.