基于遗传算法最佳阈值分割的矿石图像分割

Ore Image Segmentation Based on Optimal Threshold Segmentation Based on Genetic Algorithm

为了实现选矿的自动化,通过利用工业摄像机对传送带上的矿石颗粒图像进行采集,Matlab对采集的图像依次进行双边滤波降噪处理,遗传算法最佳阈值分割、分水岭分割,矿石颗粒像素标定以及像素面积检测的方法研究了矿石的粒度分布。结果表明:与人工筛分的数据进行对比,显示图像处理的结果在误差允许范围内。可见运用图像处理的方法进行矿石粒度的检测是可行的,能够为矿石的破碎提供数据指导,促进矿业生产的自动化和智能化。

In order to realize the automation of beneficiation,industrial cameras to collect the image of ore particles on the conveyor belt,Matlab sequentially performs bilateral filtering and noise reduction processing on the acquired images,optimal threshold segmentation of genetic algorithm,watershed segmentation,ore particle pixel calibration and pixel area was used to investigate the particle size distribution of the ore. The results show that compared with the data of artificial screening,the results of image processing were shown to be within the error tolerance. It is concluded that it is feasible to use the image processing method to detect the ore particle size,which can provide data guidance for the crushing of ore and promote the automation and intelligence of mining production.