基于质心−凸包−自适应聚类法的浮选泡沫动态特征提取

魏凯; 王然风; 王珺; 韩杰; 张茜

doi:10.13272/j.issn.1671-251x.18182

基于质心−凸包−自适应聚类法的浮选泡沫动态特征提取

doi: 10.13272/j.issn.1671-251x.18182

太原理工大学矿业工程学院，山西太原　030024

基金项目: 国家自然科学基金项目（52274157）；内蒙古自治区重点专项项目（2022EEDSKJXM010）；山西省重点研发计划项目（202102100401015）。

详细信息

作者简介:
魏凯（1997—），男，福建福州人，硕士研究生，主要研究方向为机器视觉与人工智能，E-mail：wk19971212@163.com

通讯作者:
王然风（1970—），男，山西长治人，副教授，博士，主要研究方向为智能分选，E-mail:wrf197010@126.com。

中图分类号: TD94
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出版历程
- 收稿日期: 2024-02-27
- 修回日期: 2024-08-19
- 网络出版日期: 2024-09-06

Dynamic feature extraction for flotation froth based on centroid-convex hull-adaptive clustering

College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China

摘要

摘要: 面对复杂的浮选现场环境及浮选泡沫自身相互粘连导致的边界不清等情况，现有泡沫动态特征（流动速度和崩塌率）提取方法往往无法准确划定属于每个泡沫的动态特征采样区域、不能全面匹配相邻帧间的特征点对且难以有效识别崩塌区域。针对上述问题，提出了一种基于质心−凸包−自适应聚类法的浮选泡沫动态特征提取方法。该方法采用集成Swin−Transformer多尺度特征提取能力的改进型Mask2Former，实现对泡沫质心的精准定位和崩塌区域的有效识别；通过最优凸包评价函数搜寻目标泡沫周围相邻一圈泡沫质心构建的凸包，拟合出接近实际泡沫轮廓的动态特征采样区域；运用基于Transformer的局部图像特征匹配（LoFTR）算法匹配相邻帧图像间的特征点对；针对动态特征采样区域内部的所有特征点对，通过基于OPTICS算法的主特征自适应聚类法提取每个泡沫的主要流动速度。实验结果表明，在普通泡沫质心定位和崩塌区域识别任务中，该方法分别取得了88.83%，97.92%的准确率及77.90%，96.52%的交并比；以2.69%的平均剔除率实现了99.93%的特征点对匹配正确率；在多种工况下均能有效划定与实际泡沫边界相近的特征采样区域，进而定量提取每个泡沫的动态特征。
- 浮选泡沫动态特征 /
- 泡沫图像 /
- 泡沫质心定位 /
- 泡沫崩塌区域识别 /
- 特征点对匹配 /
- 主特征自适应聚类
Abstract: In the face of complex flotation site environments and issues such as unclear boundaries caused by the mutual adhesion of flotation froth, existing methods for extracting dynamic features (such as flow velocity and collapse rate) often fail to accurately delineate the dynamic feature sampling regions corresponding to each froth, cannot comprehensively match feature points between adjacent frames, and have difficulty effectively identifying collapse regions. To address these problems, a dynamic feature extraction method for flotation froth based on a centroid-convex hull-adaptive clustering approach is proposed. This method employs an improved Mask2Former, integrated with the multi-scale feature extraction capability of Swin-Transformer, to accurately locate froth centroids and effectively identify collapse regions. An optimal convex hull evaluation function is used to search for the convex hull formed by the centroids of adjacent froth surrounding the target froth, thereby fitting a dynamic feature sampling region close to the actual froth contour. The local feature matching with transformer (LoFTR) algorithm is applied to match feature point pairs between adjacent frames. For all feature point pairs within the dynamic feature sampling region, the main flow velocity of each froth is extracted using the main feature adaptive clustering method based on the OPTICS algorithm. Experimental results show that this method achieves accuracy rates of 88.83% and 97.92% and intersection over union (IoU) rates of 77.90% and 96.52% in ordinary froth centroid location and collapse region identification tasks, respectively. It also achieves a correct feature point pair matching rate of 99.93% with an average exclusion rate of 2.69%. The method effectively delineates feature sampling regions close to the actual froth boundaries under various conditions, enabling the quantitative extraction of each froth's dynamic features.
- flotation froth dynamic features /
- froth image /
- froth centroid location /
- froth collapse region identification /
- feature point pair matching /
- main feature adaptive clustering

HTML全文

图 1 基于质心−凸包−自适应聚类法的浮选泡沫动态特征提取方法原理

Figure 1. Principle of dynamic feature extraction method for flotation froth based on centroid-convex hull-adaptive clustering

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图 2 实验系统组成

Figure 2. Experimental system setup

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图 3 不同成像条件下质心分类定位及崩塌区域识别效果

Figure 3. Classification and localization of centroid and identification of collapse region under different imaging conditions

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图 4 不同算法的特征点对匹配结果

Figure 4. Matching results of feature point pairs under different algorithms

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图 5 浮选泡沫动态特征采样区域划定效果

Figure 5. Delineation effectiveness of sampling regions for dynamic feature of flotation froth

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图 6 浮选泡沫动态特征量化效果

Figure 6. Quantification effect of dynamic features of flotation froth

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表 1 不同算法在质心分类定位与崩塌区域识别任务的性能对比

Table 1. Performance comparison of different algorithms in centroid classification positioning and collapse region identification tasks %

算法	普通泡沫质心		较小泡沫质心		不成泡区域		崩塌区域
算法	交并比	准确率	交并比	准确率	交并比	准确率	交并比	准确率
OCRNet	61.02	67.36	45.92	54.07	87.22	89.81	84.58	87.59
PSPNet	72.46	82.16	54.19	64.32	91.41	95.42	87.27	91.69
DeepLabV3+	71.22	81.07	55.33	69.89	90.20	94.09	88.05	93.19
CCNet	60.71	73.56	34.44	44.39	73.80	74.89	72.14	76.68
Segmenter	66.27	77.66	59.24	73.15	84.89	93.43	79.93	95.19
SegFormer	62.03	72.58	42.66	51.70	88.94	95.16	87.87	92.63
改进型Mask2Former	77.90	88.83	69.65	87.78	96.68	97.11	96.52	97.92

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表 2 不同算法下特征点对提取结果对比

Table 2. Comparison of extraction results of feature point pairs under different algorithms

算法	平均特征点检测数/个（±标准差）	平均错误点剔除数/个（±标准差）	平均剔除率/%	平均特征点最终匹配数/个（±标准差）	平均匹配正确率/% （±标准差）
SIFT+FLANN	1 669.37 （±303.41）	803.12 （±214.20）	48.11	866.25 （±355.23）	98.35 （±0.47）
SIFT+BF		461.63 （±82.65）	27.65	1 207.75 （±278.69）	78.80 （±10.11）
SIFT+RANSAC		830.75 （±250.49）	49.76	838.63 （±378.71）	98.93 （±0.37）
SURF+FLANN	2 131.88 （±166.28）	990.38 （±278.63）	46.46	1 141.5 （±373.15）	99.40 （±0.44）
SURF+BF		601.50 （±125.63）	28.21	1 530.38 （±227.45）	85.08 （±9.32）
SURF+RANSAC		1049.75 （±273.11）	49.24	1 082.12 （±376.68）	99.02 （±0.36）
AKAZE+BF	1 279.75 （±260.92）	236.63 （±101.56）	18.49	1 043.125 （±247.66）	92.90 （±6.58）
AKAZE+ORB		221.88 （±103.37）	17.34	1 057.88 （±252.56）	94.81 （±4.88）
AKAZE+GMS		354.75 （±173.71）	27.72	925.0 （±282.74）	99.85 （±0.21）
LoFTR	3 510.88 （±118.99）	94.37 （±62.04）	2.69	3 416.50 （±277.95）	99.93 （±0.33）

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