Improved Faster R-CNN Network for Liquid Bag Foreign Body Detection

Abstract

The production quality of medical fluid bags is closely related to patient health. In this paper, we used medical fluid bags to detect whether they contained foreign bodies. A visual acquisition system for the fluid bag was built. Vignetting correction was performed on the acquired images, and a foreign body recognition detection method based on an improved Faster R-CNN model was proposed. The feature extraction network of Faster R-CNN was discussed and studied regarding the characteristics of small foreign objects in liquid bags, and the ResNet152 network replaced the VGG16 network; furthermore, the feature fusion and attention mechanism were added to the feature extraction, and CIoU replaced the IoU loss function; the anchor box parameters were optimized and improved using the K-means clustering algorithm, and ROI Align replaced the ROI Pooling module. The improved network in this paper was compared with the Faster R-CNN model, which is a modification of feature extraction networks, such as ResNet50, ResNet101, and ResNet152, and the original VGG16 feature extraction network. The results show that the ResNet152 network had the best feature extraction effect among the feature extraction networks, and other optimizations were performed in this paper based on the use of ResNet152. In the precision−recall curve, the network in this paper showed the best effect. The improved algorithm presented in this paper was significantly improved compared with the original algorithm, with a detection accuracy of 97% and an average accuracy improvement of 7.8% in foreign object recognition.