基于神经批采样的轮胎X光图像异常检测研究

轮胎缺陷检测对轮胎定级有重要参考意义,研究高性能的轮胎异常检测方法尤为重要。本文以强化学习算法为基础,提出一个以损失值异常变化作为判断异常特征的图像自动分类算法。该方法首先通过大量的正向样本输入来降低数据在经过梯度更新之后的损失值,从而与少量异常样本输入时的损失值形成明显差异,再引入神经批采样器,放大异常样本与正向样本之间的损失轮廓差异并为空间变分编码器提供训练批次,然后将训练结果作为异常分类器的输入,最后完成异常检测的分类与定位工作,经过对比研究发现本文提出的异常检测算法在轮胎缺陷样本集上性能明显优于其他传统图像异常检测方法。

Research on tire X-ray image anomaly detection based on neural batch sampling

Tire defect detection has important reference significance for tire grading, and it is particularly important to study the high performance tire anomaly detection method. Based on reinforcement learning algorithm, an automatic image classification algorithm based on abnormal loss value is proposed. This method firstly by a large number of positive samples input to reduce the loss value of data after gradient update, with a small amount of the loss of the abnormal sample input values form the obvious difference, introducing neural sampler, enlarge abnormal loss of contour difference between samples and the positive samples and provide training to sVAE batch, then put the training result as input of abnormal classifier, Finally, the classification and location of anomaly detection are completed. Through comparative study, it is found that the anomaly detection algorithm proposed in this paper is obviously superior to other traditional image anomaly detection methods in tire defect sample sets.