基于改进Mask R-CNN的矿石类型检测算法

Ore type detection algorithm based on improved Mask R-CNN

针对不同的矿石类型进行选矿工艺参数设置和操作有利于保障精矿质量,提高回收率和降低物资消耗。由于缺乏矿石类型实时检测有效手段,选矿过程的精准操作目前较难实现。为此,本文提出一种全面改进的Mask R-CNN矿石类型检测算法。算法步骤:①使用ResNetV1d-50提取矿石图像各阶段的特征图,并在主干网络中加入可变形卷积以便增强异形矿石的特征;②改进FPN,通过在主干网络的C5特征层加入特征残差模块,并融合到P5特征层,得到具有更强语义信息的多尺度特征图;③改进RPN,设计自适应的正样本IOU选取方案来匹配宽高比异常的矿石,进一步提高异形矿石的识别精度;④在RoIAlign网络基础上加入Global-Context,以提高小矿石的检测能力;⑤在数据增强和训练技巧方面对模型进行改进。结果表明,本文算法的平均精度为67.92%,平均交并比为63.54%,分别比基准模型提高了13.67%和9.71%。本文研究方法在矿石类型识别领域具有较好的应用价值。

The setting and operation of beneficiation process parameters for different ore types are conducive to ensuring the quality of concentrate,improving the recovery rate and reducing the material consumption.Due to the lack of effective means for real-time detection of ore types,it is difficult to achieve accurate operation in the beneficiation process.Therefore,a comprehensive improved Mask R-CNN ore type detection algorithm is proposed.Algorithm steps:1)ResNetV1d-50 is used to extract the feature maps of each stage of the ore image,and deformable convolutions are added to the backbone network to enhance the characteristics of the special-shaped ore;2)To improve FPN,a multi-scale feature map with stronger semantic information is obtained by adding a feature residual module to the C5 feature layer of the backbone network and fusing it into the P5 feature layer;3)Improve the RPN,and design an adaptive positive sample IOU selection scheme to match the ore with abnormal aspect ratio,so as to further improve the identification accuracy of special-shaped ore;4)Global-Context is added on the basis of the RoIAlign network to improve the detection ability of small ores;5)The model is improved in terms of data augmentation and training skills.The results show that the average accuracy of this algorithm is 67.92%,and the average intersection ratio is 63.54%,which is 13.67%and 9.71%higher than the benchmark model.The research method has good application value in the field of ore type identification.