Simulations of Bubble Formation and Oscillation Behavior in Micro-Leakage Measurement System

Ding, Zhenglong; Yin, Zongjun; Song, Wan; Cheng, Wenjuan; Xu, Jiajia; Shu, Zhan

doi:10.1109/tim.2023.3261926

Cited by 1 publication

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gas-liquid two-phase reactions are very common in industrial processes, such as chemical engineering, energy, and wastewater treatment. For such reaction, studying bubble detection methods is of great significance for obtaining the changes in the gasliquid interface [1] [2]. Industrial sealed containers are a common type of gas storage equipment, such as natural gas storage tanks, oxygen cylinders, etc.…”

Section: A Problem Significancementioning

confidence: 99%

Research on Bubble Detection Based on Improved YOLOv8n

Chen,

Zeng

2024

IEEE Access

View full text Add to dashboard Cite

Bubble detection has important applications in industries such as tightness testing, fluid measurement, healthcare, and chemical engineering. Due to the fact that bubbles belong to small targets with characteristics such as reflection and shadows, they have fewer available features and are highly susceptible to environmental interference, which makes it difficult for detection models to accurately locate and recognize the bubbles. Aiming at these problems, an improved YOLOv8n for bubble detection is proposed. Firstly, the deformable convolution network (DCN) is used in the Backbone module to take place of the original C2f module, which enables the model to have stronger feature extraction and adaptive generalization capabilities for small targets with different deformations. Then the global attention mechanism (GAM) is introduced into Neck network to locally enhance the channels or regions of interest, which is beneficial for capturing the important features of targets, especially small targets like bubbles. Finally, the loss function is improved so that boundary box regression and target detection become more accurate. Experiments are conducted on the bubble dataset, and the results indicate the mean average precision (mAP) of the improved algorithm reaches 97.4%, increasing by 2.2% compared to the original YOLOv8n.This shows the proposed method has better comprehensive performance on bubble detection.

show abstract

Section: A Problem Significancementioning

confidence: 99%