Version 1
: Received: 8 August 2024 / Approved: 9 August 2024 / Online: 14 August 2024 (11:45:54 CEST)
How to cite:
Sharma, A.; Eickhoff, J. M.; Echterhof, T.; Wuppermann, C. Deep Learning Approach for Bubble Detection and Segmentation with YOLOv5. Preprints2024, 2024081050. https://doi.org/10.20944/preprints202408.1050.v1
Sharma, A.; Eickhoff, J. M.; Echterhof, T.; Wuppermann, C. Deep Learning Approach for Bubble Detection and Segmentation with YOLOv5. Preprints 2024, 2024081050. https://doi.org/10.20944/preprints202408.1050.v1
Sharma, A.; Eickhoff, J. M.; Echterhof, T.; Wuppermann, C. Deep Learning Approach for Bubble Detection and Segmentation with YOLOv5. Preprints2024, 2024081050. https://doi.org/10.20944/preprints202408.1050.v1
APA Style
Sharma, A., Eickhoff, J. M., Echterhof, T., & Wuppermann, C. (2024). Deep Learning Approach for Bubble Detection and Segmentation with YOLOv5. Preprints. https://doi.org/10.20944/preprints202408.1050.v1
Chicago/Turabian Style
Sharma, A., Thomas Echterhof and Christian Wuppermann. 2024 "Deep Learning Approach for Bubble Detection and Segmentation with YOLOv5" Preprints. https://doi.org/10.20944/preprints202408.1050.v1
Abstract
Interaction between multiple phases especially liquid-gas is one of the most common phenomena in industrial systems. Computer Vision enables modern systems to derive an automated and robust image-processing technique to address this issue. However, diverse experimental conditions still make it a challenging task to overcome. In Deep Learning, instance segmentation has created a niche in various computer vision applications as it aims to provide different instance IDs to objects belonging to the same class. In this paper, we propose a novel approach employing the YOLO (You Look Only Once) an end-to-end deep learning architecture for the detection and segmentation of bubbles in fluid flows. YOLOv5 offers significant improvements in both speed and accuracy compared to its predecessors like YOLOv3. Therefore, we train the YOLOv5 network on a dataset consisting of annotated images representing instances of bubbles across varied fluid flow environments. This paper provides both qualitative and quantitative evaluation of three YOLOv5 networks (small, medium, and extra-large) in detecting and segmenting bubbles with high accuracy and efficiency. Based on the metrics all three YOLOv5 models achieved high precision above 0.98 for bounding box detection and 0.97 for segmentation mask, and high recall value greater than 0.97 for box and 0.96 for mask quality was achieved. The achieved results indicate that the models accurately localize bubbles within the image frame, and distinguish bubble pixels from the background. Also, mean average precision (mAP50-95) consistently exceeds 0.84 across all models for the bounding box and 0.73 for the segmentation mask. The ascertained results emphasize the potential of YOLOv5 architecture as a powerful tool for automated bubble analysis in real-world applications. Our research contributes to advancing the field of deep neural networks in fluid systems and provides a fresh approach for future developments in automated bubble analysis.
Engineering, Metallurgy and Metallurgical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
