Research On Visual Detection Technology Of Foreign Matter In Medicinal Liquid Based On Deep Learning

In the pharmaceutical manufacturing process,due to factors such as workshop environment and complex production process,it is easy to mix foreign matters such as hair and glass debris into the liquid medicine.If such products are not detected during the manufacturing process,they may cause physical harm to patients after flowing to the market.Therefore,the detection of foreign matters in the liquid medicine is an important step to ensure the quality of medicines and achieve high-efficiency production.The foreign bodies in the liquid medicine are generally small.In the process of detecting,there are some problems such as low accuracy and high missed detection rate.In this paper,the above problems are studied,and the algorithm proposed in this paper is verified by experiments.1.Introduce the research background and research significance of the intelligent detection of medicine quality,introduce the advanced drug quality detection equipment at home and abroad,introduce the research status at home and abroad of the visual detection algorithm for foreign objects in the liquid medicine,and sorts out the common types of foreign bodies in liquid medicine.The imaging scheme,machine vision equipment selection and electrical control design in the developed medicine quality inspection system are described.2.Focus on the visual detection algorithm of liquid foreign matter based on Retina Net.The framework of Retina Net is introduced,and its feature extraction module,feature fusion module and foreign object detection module are analyzed in detail.Experiments are carried out on the foreign matter detection algorithm based on Retina Net,the image preprocessing and labeling of data set and the training process of retinanet network are described,and the experimental results are analyzed from seven dimensions of average precision,average recall,average missed detection rate,calculation amount,parameter amount,average training time,and the number of frames processed per second.3.Aiming at the problem that the foreign matter in the liquid medicine is small and difficult to detect,this paper proposes a feature fusion method of foreign matter in the liquid medicine based on attention enhancement,and improves the foreign matter feature fusion module in the Retina Net structure.The foreign object feature fusion methods based on residual feature enhancement and attention enhancement are analyzed,and the two methods are introduced into Retina Net.The experimental results show that under the same data set and Io U threshold setting conditions,the two feature fusion methods have improved the detection accuracy of foreign matter in liquid medicine,and the latter method has obvious advantages.4.The selection of positive and negative training samples is directly related to the accuracy of foreign object detection.Based on the selection of positive and negative samples,this paper improves the liquid medicine foreign body detection module in the Retina Net structure,and introduces an adaptive selection method of positive and negative samples based on PAA.Liquid foreign body detection method.Common positive and negative sample selection methods include: positive and negative sample allocation based on Io U threshold,positive and negative sample allocation based on anchor-free,positive and negative sample selection based on ATSS adaptation,and positive and negative sample selection based on PAA adaptation.Four positive and negative sample selection methods are compared: positive and negative sample allocation based on IOU threshold,positive and negative sample allocation based on anchor free,positive and negative sample selection based on ATSs adaptive and positive and negative sample selection based on PAA adaptive.It is found that the positive and negative sample selection algorithm based on PAA adaptive is more suitable for the detection of foreign bodies in liquid medicine.Finally,this paper integrates the improved foreign body feature fusion module and foreign body detection module into the Retina Net structure.The experimental results show that,compared with Retina Net,the improved foreign body feature fusion module and the improved foreign body detection module algorithm,the improved liquid foreign body detection method has obvious optimization in precision,recall rate and missed detection rate.