Defect Detection of Tire X-ray Images Based on Image Processing
The defects in tire X-ray images were detected automatically by using deep learning methods based on image processing.Tire X-ray images had the characteristics of high resolution,narrow shape and small defect targets.By cutting each X-ray image into 640X640 pixels and annotating each cutted region,the defective regions were divided into a training set,and the training set was histogram balanced to enhance the contrast between the foreground and background of the image.Further data augmentation was performed on the training set to improve the model's generalization ability.Finally,the optimal weights were trained on the Faster R-CNN deep learning defect detection model.In the model inference stage,the complete X-ray image would be fed into the model,the defect range would be framed,and reassembled into the original X-ray image,and if a defect had multiple boxes,all adjacent boxes were combined into one box.The method could effectively reduce the missed detection rate of small defect targets,improve the accuracy of detection,and indirectly solve the problem of feature loss in the original X-ray image.
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