Microfluidic chip foreign body detection based on improved YOLOx

Abstract

To solve the problem of identifying the presence of foreign objects in microfluidic chip images, an improved model is proposed for the feature of small foreign object targets. The attention mechanism is introduced to enhance the perceptiveness of the model in channel and space. The ResUnit module in the network is modified to enhance the feature information. Also choose diou as the loss function to improve the edge accuracy. The experimental results show that the improved YOLOx target detection algorithm has a significant improvement in foreign object detection in terms of accuracy, and the average precision (AP) reaches 99.12% on YOLOx, which is 0.7% higher than the original network. The results show that the improved algorithm based on YOLOx in this study can achieve foreign object detection in microfluidic chip images.