摘要
针对人工核药效率低的问题,提出了一种改进MobilenetV3-Small网络结构的药片定位识别方法。将获取的原始图片转换到LAB颜色空间,对LAB颜色空间下的L通道进行Canny边缘分割、形态学处理、图像填充、单一药片图像框选,实现对单一药片的定位;通过加载预训练权重,使用K-means聚类算法优化输入尺寸,根据特征图修改网络层等方法改进MobilenetV3-Small网络结构;结合图像处理的定位方法和改进MobilenetV3-Small网络的识别方法,实现对药片的快速准确检测。最终在处理得到的523张单一药片图像上,药片识别算法最终Loss值为0.000035,验证了算法的有效性。
In response to the problem of low efficiency of manual verification of medicines, a method for recognizing tablet position with improved MobilenetV3-Small network structure is proposed. The acquired original images are converted to the LAB color space, and the L channel under the LAB color space is employed with canny edge segmentation, morphological processing, image filling and single pill image frame selection, so as to position a single pill;the network structure of the MobilenetV3-Small is improved by loading pre-trained weights, using the K-means clustering algorithm to optimize the input size, modifying the network layer according to the feature map, etc.;by combining with the positioning method of image processing and the recognition method of improved MobilenetV3-Small network, the rapid and accurate detection of tablets is realized. Finally, on the 523 single pill images processed, the Loss value of the pill recognition is 0.000035, which verified the effectiveness of the algorithm.
作者
李金桥
王文韫
夏凯凯
王财江
LI Jinqiao;WANG Wenyun;XIA Kaikai;WANG Caijiang(School of Mechanical and Electrical Engineering,Hunan University of Science and Technology,Xiangtan 411201,China;Hunan Provincial Key Laboratory of Mechanical Equipment Health Maintenance,Hunan University of Science and Technology,Xiangtan 411201,China)
出处
《光学技术》
CAS
CSCD
北大核心
2023年第1期91-96,共6页
Optical Technique
基金
湖南省自然科学基金面上项目(2021JJ30251)
湖南省自然科学基金青年项目(2020JJ5187)
中央引导地方科技发展资金项目(2022ZYT012)。
