Deep Multimodel Cascade Method Based on CNN and Random Forest for Pharmaceutical Particle Detection

Abstract

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.