Bougie location algorithm of lattice biochip based on YOLOv5

Abstract

Bougie location is the crucial step of information extraction in biochips. Many researchers have used algorithms, such as image brightness information or autocovariance, to grid the biochip images. However, these algorithms cannot resolve problems such as the bougie lattice being tilted or there being some bougies with insufficient hybridization. To resolve such problems, we propose a grid location algorithm based on the output of the fifth version of You Only Look Once (YOLOv5) for bougies in lattice biochip images. First, we use the YOLOv5 algorithm to detect and locate the bougies with sufficient hybridization. Second, we build a linear model for bougie location based on the least squares method and use the depth-first search algorithm to detect and locate the bougies with insufficient hybridization. Finally, we use Otsu’s method to extract the gray values of grid areas. The algorithm was tested on a dataset consisting of 100 lattice biochip images. The experimental results show that the average accuracy is 99.76%, and the average detection time is 5.04 s. The algorithm has good robustness and high accuracy and can accurately locate the bougies.