Target detection method for bubbles in plate heat exchanger

Abstract

The invention discloses a target detection method for bubbles in a plate heat exchanger, and aims to solve the technical problems that a traditional detection method is complex in detection process, large in calculated amount and poor in detection effect. Two-phase flow flowing videos in a lower plate heat exchanger are shot through a high-speed camera to make a bubble data set; a Darknet learningframework is utilized, a YoLov3-tiny model is adopted, parameters before a full connection layer are frozen through transfer learning, and the model is utilized as a feature extractor; a target detection problem in the scene is taken as a regression prediction problem of the target; an improved three-frame difference method is added, so that the recognition rate is improved; and repeated candidate boxes are removed by using an IoU score screening algorithm, and then a final detection result is obtained. According to the method, the calculated amount of image features after transfer learning is used is reduced, the detection process is simplified, the detection speed is high, the network recognition precision is improved, and the detection result is more comprehensive.

Description

technical field [0001] The invention relates to the technical field of target information detection, in particular to a target detection method for air bubbles in a plate heat exchanger. Background technique [0002] In recent years, with the increasing demand for energy conservation and environmental protection, how to improve the heat exchange efficiency of heat exchange equipment has become a key issue of concern. The state of the two-phase flow has a great influence on the heat transfer performance of the heat exchanger, so the visualization of the two-phase flow in the heat exchanger becomes very important. [0003] Bubble target detection, as a key technology in the study of two-phase flow, has been widely concerned by scholars at home and abroad for a long time. Target detection technology is an important component in the field of computer vision. Using the target detection method can realize the visualization of the two-phase flow in the flow channel, which is conve...

AI Technical Summary

Problems solved by technology

However, the limitation of this method is that it needs to manually extract features, and it is impossible to directly judge the impact of the extracted features on the performance of the entire target detection system. Therefore, researchers need to conduct in-depth research to design robust features.

Moreover, the above-mentioned traditional methods have extremely high requirements on the experience of technicians, and the amount of calculation is very huge, and the operation process is complicated.

[0005] In addition, some scholars use deep learning methods for bubble target detection, and use convolutional neural networks combined with sliding window methods to detect bubbles in pictures. Huge, slow to detect

Images