摘要
为了识别可冶炼铀矿石,提高资源利用率,采用X射线透射技术,并结合半监督学习算法—ITSVM,实现对铀矿的智能分选。同时为进一步优化模型性能,引入了亮暗校正方法以解决图像噪声问题,该方法通过归一化处理,将噪声图片中的每个像素点进行映射,从而提升图像质量。通过改进的直线凹点检测与切割算法和切片方法,攻克了支持向量机对多目标分类任务的难题,该算法通过检测像素点相对于直线的位置和距离,利用约束条件判断凹点,采用最小距离切割方法获得对应的切割线,再通过切片的方法将多目标检测问题转化为多个独立的单一目标检测问题。通过综合这2种优化方法,最终建立了ITSVM铀矿分选模型。通过X射线投射技术收集到的2000张铀矿图片对该模型进行训练测试,并与SVM和TSVM模型进行结果对比。结果表明,经过亮暗校正,模型在检测铀矿的准确性方面提升了2.9个百分点;通过使用改进的直线凹点检测与切割算法和切片方法,使ITSVM模型具备多目标检测功能,模型对多目标铀矿图片检测的准确性达到95.7%;在测试集上,ITSVM模型检测铀矿的准确性达到97.3%。相比于SVM和TSVM,ITSVM在检测铀矿的准确性和持续优化模型方面具有更大优势,实验结果验证了ITSMV模型在铀矿分选领域的可行性。
In order to identify extractable uranium ore and enhance resource utilization efficiency,utilizing X-ray trans-mission technology in conjunction with the semi-supervised learning algorithm-ITSVM,to achieve intelligent uranium ore sor-ting.At the same time,in order to further optimize the performance of the model,the light and dark correction method is intro-duced to solve the problem of image noise.This method maps each pixel in the noisy image through normalization processing,so as to improve the image quality.Through the improved straight concave detection and cutting algorithm and slicing method,the difficult problem of multi-objective classification task of support vector machine is overcome.The algorithm detects the posi-tion and distance of pixels relative to the straight line,uses constraints to judge the concave point,and obtains the correspond-ing cutting line by using the minimum distance cutting method.Then the multi-target detection problem is transformed into sev-eral independent single-target detection problems by slicing method.By synthesizing these two optimization methods,the ITSVM uranium ore sorting model is finally established.The model was trained and tested by 2000 uranium ore images collected by X-ray projection technology,and the results were compared with SVM and TSVM models.The test results show that the accuracy of the model in detecting uranium ore is improved by 2.9 percentage points after light and dark correction;The ITSVM model has the function of multi-target detection by using improved straight concave detection and cutting algorithm and slicing meth-od,and the accuracy of the model for multi-target uranium mine image detection reaches 95.7%.On the test set,the accuracy of ITSVM model to detect uranium ore reaches 97.3%.Compared with SVM and TSVM,ITSVM has greater advantages in the accuracy of uranium ore detection and continuous optimization model.The experimental results verify the feasibility of ITSMV model in the field of uranium ore sorting.
作者
吴泽彬
陈锐
WU Zebin;CHEN Rui(School of Mechanical and Electronic Engineering,East China University of Technology,Nanchang 330013,China;Jiangxi Engineering Research Center of Process and Equipment for New Energy,Nanchang 330013,China)
出处
《金属矿山》
CAS
北大核心
2024年第3期229-236,共8页
Metal Mine
基金
国家自然科学基金项目(编号:U22B2077)
江西省科技厅重大科技研发专项(编号:20224AAC01012)。
