Rotter Pawel, Klemiato Maciej, Knapik Dawid, Rosół Maciej, Putynkowski Grzegorz
AGH University of Krakow, al. Mickiewicza 30, 30-059 Krakow, Poland.
CBRTP S.A.-Centrum Badań i Rozwoju Technologii dla Przemysłu S.A., ul. Ludwika Waryńskiego 3A, 00-645 Warszawa, Poland.
Sensors (Basel). 2024 Nov 7;24(22):7150. doi: 10.3390/s24227150.
The algorithm and prototype presented in the article are part of a quality control system for plastic objects coming from injection-molding machines. Some objects contain a flaw called inclusion, which is usually observed as a local discoloration and disqualifies the object. The objects have complex, irregular geometry with many edges. This makes inclusion detection difficult, because local changes in the image at inclusions are much less significant than grayscale changes at the edges. In order to exclude edges from calculations, the presented method first classifies the object and then matches it with the corresponding mask of edges, which is prepared off-line and stored in the database. Inclusions are detected based on the analysis of local variations in the surface grayscale in the unmasked part of the image under inspection. Experiments were performed on real objects rejected from production by human quality controllers. The proposed approach allows tuning the algorithm to achieve very high sensitivity without false detections at edges. Based on input from the controllers, the algorithm was tuned to detect all the inclusions. At 100% recall, 87% precision was achieved, which is acceptable for industrial applications.
本文中介绍的算法和原型是注塑机生产的塑料制品质量控制系统的一部分。一些塑料制品存在一种名为夹杂物的缺陷，通常表现为局部变色，这会导致产品不合格。这些塑料制品具有复杂、不规则的几何形状且有许多边缘。这使得夹杂物检测变得困难，因为夹杂物处图像的局部变化比边缘处的灰度变化要小得多。为了在计算中排除边缘，所提出的方法首先对物体进行分类，然后将其与相应的边缘掩码进行匹配，该掩码是离线准备并存储在数据库中的。基于对被检查图像未掩码部分表面灰度局部变化的分析来检测夹杂物。实验是对人工质量控制人员判定为生产不合格的真实物体进行的。所提出的方法能够对算法进行调整，以实现非常高的灵敏度，同时在边缘处无误检测。根据控制人员的反馈，对算法进行了调整以检测所有夹杂物。在召回率为100%时，精度达到了87%，这对于工业应用来说是可以接受的。
