人工智能
计算机科学
随机森林
计算机视觉
卷积神经网络
聚类分析
目标检测
模式识别(心理学)
弹道
级联
帧速率
帧(网络)
特征提取
工程类
物理
电信
化学工程
天文
作者
Hui Zhang,Miao Zhao,Li Liu,Hang Zhong,Zhicong Liang,Yimin Yang,Xianen Zhou,Q. M. Jonathan Wu,Yaonan Wang
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2020-09-01
卷期号:69 (9): 7028-7042
被引量:23
标识
DOI:10.1109/tim.2020.2973843
摘要
The quality detection of pharmaceutical liquid products is inevitable and crucial in drug manufacture because drugs contaminated with foreign particles are definitely not to be used. However, with the current detection methods, it is still a challenge to detect and identify the small moving particles using an imaging system. In this article, a deep multimodel cascade method combining single-frame image and multiframe images processing method to detect and identify foreign particles is proposed. The proposed method consists of three stages. First, a Faster R-CNN convolutional neural network is adopted to detect and localize the multiple suspected foreign particles of each single-frame image. Then, the k-means clustering algorithm is used to cluster the trail of that detected multiple suspected foreign particles in the eight sequential images to obtain the moving object trajectory. Finally, trajectory features are extracted and the random forest (RF) classifier is used to distinguish noises and foreign particles according to the motion feature of the moving object trajectory. Experimental results demonstrate that the proposed multitask stepwise method improves the accuracy of foreign particles detection and reduces the rate of omission in the case of strong noise, which proves the effectiveness of this method.
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