针对浮选表面泡沫流动变化和运动形变导致崩塌率难以检测的问题，提出了一种在非下采样 Shearlet 变换 (NSST) 域融合改进快速视网膜关键点 (FREAK) 匹配及形状全方向相似度的泡沫崩塌率检测方法。对相邻两帧泡沫图像进行 NSST 分解，分割低频子带图像的气泡亮点，在多尺度高频子带结合方向模极大值检测和非极大值抑制进行特征点检测，改进 FREAK 采样模型并用于特征点描述及匹配，通过统计前一帧分割亮点周围匹配点数提取潜在崩塌气泡，然后对各潜在崩塌气泡通过前后帧分割亮点的形状复杂度特征及全方向相似度计算进一步确定崩塌气泡，最后根据崩塌气泡的提取结果计算崩塌率。实验结果表明，该方法受泡沫不均匀流动、运动形变的影响小，能有效提取出崩塌气泡，检测精度较现有方法有较大提高，不同工况下均表现出良好的鲁棒性，满足浮选生产在线检测的需求。

A new froth collapse rate detection method by the fusion of fast retina keypoint (FREAK) and shape omnidirectional similarity in Nonsubsampled Shearlet transform (NSST) multiscale domain was proposed，consi-dering the difficulty to detect the froth collapse rate resulted from continuous flow and movement deformation． First-ly，two adjacent froth images were decomposed through NSST，froth bright spots are extracted by segmentation of low frequency subband image． And feature points were tested by direction modulus maxima detection and nonmaxi-mum suppression among multiscale high frequency subbands，then FREAK sampling pattern was improved and used for feature points description and matching． Secondly，potential collapsed bubbles were extracted according to the number of matching points that around the bright spots of previous frame，and then collapsed bubbles were selected from potential collapsed bubbles by using shape complexity feature and omnidirectional similarity detection
of bright spots between previous frame and next frame． Finally，the bubble collapse rate was calculated according to the detection results of collapsed bubble． Experimental results show that，the proposed method is affected little by nonuniform flow and movement deformation of bubbles and can effectively extract all collapsed bubbles． Be-sides，it achieves not only a better detection accuracy than that of existing methods，but also robustness of per-formance under different flotation working condition，thus this method meets the on-line detection need of flotation production．