一种基于目标特征挖掘的带钢缺陷图像分割方法

A Segmentation Method of Strip Defects Image Based on Characteristics Mining of Targets

通过分析带钢图像,挖掘缺陷图像的特征,提出一种基于目标特征挖掘的带钢缺陷图像分割方法。首先,将采集的带钢缺陷图像进行中值滤波处理;然后,通过等分图像灰度范围所确定的一系列阈值对带钢图像进行预分割,通过挖掘带钢缺陷图像的特征,以特征因子作为任务驱动,找出特征值发生突变的区间;在此突变的区间内,再按照上述方法对带钢图像缺陷存在的区域进行细分,找出面积突变点,以此确定最佳阈值,通过最佳阈值进行带钢缺陷图像分割,得到特征子图;最后将若干特征子图融合,得到带钢缺陷图像分割结果。实验结果表明,将此方法应用于带钢缺陷图像分割过程中,能够完整有效地分割出带钢缺陷区域,为带钢缺陷的视觉在线检测提供了可能性。

Through the analysis of strip image and characteristics mining of defect image,this paper proposes a strip defect image segmentation method based on target characteristic mining. Firstly,this program pretreats strip defect image with median filter algorithm,then segments the strip image with a series of threshold,through characteristics mining of strip defect image and taskdriven,finds out the eigenvalue mutation interval. In this mutation interval,the program segments the strip image with above method again to subdivide and finds out the optimal threshold. The program segments the strip image with the threshold and gets characteristic sub graph. Lastly,the program fuses characteristic sub images and gets the segmentation results. Experiments show that applying this method to the strip defect image segmentation process can effectively segment strip defects image. It means that this method provides a possibility for visual detection of strip defects.