Authors

Abstract

Biomass particle is one of the most important solid briquette fuels for agricultural and forestry biomass energy. Temperature, pressure, moisture and discharge holes are important factors to control biomass particle forming. The inappropriate setting of the parameters or blocking of the discharge hole will lead to the defects of the biomass particles, such as too short or poor roundness or pits or cracks. In order to detect these defects automatically, this paper proposes a method based on K-Means with prior knowledge. Firstly, the inner boundary tracking region detection algorithm and filling algorithm are combined to extract the regions in the backlight image. The regions are divided into debris, independent biomass particle regions and adhesive biomass particle regions. Secondly, K-Means with prior knowledge is used to segment the adhesive regions to get the independent biomass particle regions. Finally, the features of the biomass particles are extracted to judge the type of defects. The proposed method has been tested on images acquired from the vision system of the ring roller pellet mill. Experimental results show the efficiency of the proposed method in high detection accuracy and short detection time.

Abstract in Chinese

生物质颗粒是农林生物质能源的一种重要的农林生物质能源固体成型燃料。温度、压力、水分和模孔是控制生物质颗粒成型的重要因素。如果参数设置不合适或者模孔堵塞，会造成生物质颗粒长度过短、圆度欠佳、凹坑、裂缝等缺陷。为了自动检测这些缺陷，本文提出一种基于先验知识的Kmeans聚类方法。首先，采用基于行扫描的内边界跟踪区域检测和填充算法提取生物质颗粒图像各独立区域。根据区域面积将各独立区域划分为秸秆碎屑、单独的秸秆颗粒区域和粘连的秸秆颗粒区域。其次，使用基于先验知识的KMeans算法将粘连的秸秆颗粒区域分割，得到独立的秸秆颗粒区域。最后，对各独立秸秆颗粒提取特征，并据此判断是否存在缺陷。算法在从秸秆颗粒生产线上采集的图片集合中进行验证。实验结果证明本文算法具有较快的检测速度和较快的检测正确率.