A Method for Detecting Adhesive Defects of Infusion Catheter Based on Improved SSD
In the assembly process of medical devices,applying an appropriate amount of catheter glue can effective-ly avoid the displacement or detachment of the catheter.To ensure the safety and stability of infusion treatment,the de-fect detection of catheter glue is an indispensable part of medical device testing.However,most of the existing catheter gluing technologies use automated control of the amount of glue to realize the catheter gluing,lacking the steps of gluing defect detection,and there are still hidden dangers of non-standard catheter gluing.In order to solve the above prob-lems,this paper proposes a deep learning-based catheter coating defect detection,which uses SSD(Single Shot MultiBox Detector)object detection algorithm to achieve the defect detection of catheter coating.At the same time,ResNet101 is used as the feature extraction network,which improves the feature modeling ability of the model and further improves the accuracy of the defect detection algorithm.The experimental results show that the method proposed is superior to the current mainstream defect detection algorithms,achieving high precision defect detection of catheter coating,which further promotes the development of catheter coating technology.
NETL
NSTL
万方数据
维普
