基于Faster R-CNN改进的数粒机系统

Improved Capsule Counting Machine Based on Faster R-CNN

目的解决目前数粒机只能计数不能同时分拣残损药粒的问题。方法设计以Faster R-CNN深度神经网络为核心的药粒数粒机系统。在原有的数粒机基础之上,更换CCD线阵相机为面阵相机,以满足图像采集的需求,进一步使用图像分割和多线程技术加快图像处理速度。最终通过训练好的Faster R-CNN网络检测出目标并分拣。结果经过测试集的验证,正常药粒识别率达到了95.47%,残损药粒识别率达到了97.94%,单幅图像处理达到了65 ms的实时速度。结论该方法在传统的计数基础上很好地融合了先进的深度学习技术,实现了目标的自动分拣。

The work aims to solve the problem that the current capsule counting machine can only count capsules and cannot sort damaged capsules at the same time. A capsule counting machine with the Faster R-CNN deep neural network as the core was designed. On the basis of the original capsule counting machine, the CCD line-array camera was replaced by area-array camera to meet the demand of image acquisition, and the image segmentation and multi-thread technology were further used to speed up the image processing speed. Finally, the target was detected and sorted through the well trained Faster R-CNN network. After verification of the test set, the identification rate of normal capsule reached 95.47% , the identification rate of damaged capsule reached 97.94% , and the single image processing reached the real-time speed of 65 ms. The proposed method properly combines the advanced in-depth learning technology based on the traditional counting and realizes the automatic sorting of the target.