雾滴图像粘连特征改进判断及分离计数方法优化

Optimized Method of Improved Characteristics Judgment and Separation Counting for Adhesive Droplets

摘要:

雾滴在靶标上常出现粘连的情况，为准确测量雾滴尺寸、掌握雾滴分布规律，需要判断雾滴是否粘连，并用图像处理技术将粘连雾滴分开。首先提出判断雾滴是否粘连的改进方法，该方法结合雾滴的形状因子和面积阈值对粘连雾滴进行判断和特征提取，并用极限腐蚀法和迭代开运算法对粘连雾滴进行计数处理，其次调用迭代开运算标记的分水岭算法分割，最后对分割后雾滴的连通域进行标记及形状圆整。试验结果表明：该方法可实现粘连雾滴的自动判断和特征提取，弱粘连准确率100%，强粘连可达97.2%以上。该算法获得的雾滴粒径参数与激光粒度仪试验测量结果接近，其尺寸测量准确度较Deposit Scan软件计算平均提高了7.67%。基于相同的样本，与人工计数标定结果对比表明，该方法获得的雾滴个数快速且精准度达97.06%以上。

Abstract:

Droplet adhesion on target is a common practice in pesticide spray. In order to accurately measure the droplet size and evaluate the distribution, adhesive droplets need to be identified and segmented by image processing. An improved method for judging the droplet adhesion and extracting the features based on droplet shape factor and area threshold of droplets was proposed in this paper. The adhesive droplets were counted by using ultimate erosion and iterative opening operation and segmented by using the watershed algorithm marked with the iterator open operation. The connection areas of the segmented droplets were marked and the shape was rounded. Experimental results showed that the method can effectively extract the characteristics of the adhesion droplets. The accuracy rate of judgment is 100% for weak adhesion and up to 97.2% for strong adhesion. The droplet size obtained by this image process is very close to that measured by laser particle size analyzer. Comparing with Deposit Scan software, this method may improve the size measurement accuracy about 7.67%. Based on the same sample, the comparative analysis showed that the proposed method may obtain the droplet numbers in a much faster speed than the artificial counting and achieved an accuracy of more than 97.06%. The research result also showed that compared with the laser particle size method, the image measurement method was more simple, effective, and suitable for measurement and statistics of the droplet parameters in the field spray experiment.