Improved Multidomain Convolutional Neural Network for Intelligent Tracking of Marangoni Effect Process Sequence Images

Abstract

Sequence image target tracking has two requirements of accuracy and speed. Aiming at the problem of slow speed of convolutional neural network (CNN) target tracking algorithm, an improved algorithm for sequence image target tracking is designed by using a fusion of Faster multidomain CNN (MDNet) and optical flow method. The target algorithm designed in this article is improved on the basis of MDNet. The optical flow method is used to obtain the primary selection box first, which can effectively deal with the challenges brought by scale change and in-plane rotation, and has good tracking speed and performance. The objective algorithm of this article is used to explore the Marangoni effect in the melting process of solid iron tailings in molten blast furnace slag. Because the main component of iron tailings is Silica, and the melting point of Silica is the highest, so taking Silica as the research object, an intelligent analysis of Marangoni effect in high-temperature melting process of Silica particles is realized. The results show that the target algorithm achieves the real-time standard (FPS > 24), the average distance precision (DP) is 97.3%, the average success rate (SR) is 98.02%, the average center location error (CLE) is 6.2684 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> m, and the average overlap precision (OP) coverage is 63.86%. The target algorithm has certain advantages in the following five evaluation indexes: DP, SR, CLE, OP, and frames per second.