Oil Particle Analysis Using Machine Learning and Holography Imaging
Abstract
Holographic cameras show potential as a sensor to monitor oil spills. Holographic cameras record the light interference from particles in a volume of space, producing an image called a hologram. Processing these holograms is known as hologram reconstruction. It produces a representation of particles located in three-dimensional space. These cameras can record precise shapes and sizes of particles in a volume of water. However, it is very time-consuming and resource-intensive to process the images. Most algorithms that perform particle analysis require the hologram reconstruction step. The well-documented hybrid method is one such algorithm. Machine learning is one possible technique that shows potential for avoiding the costly hologram reconstruction altogether while extracting particle statistics. We tested two different machine learning models, U-Net with a resnet34 backbone and YOLOv5, and compared their performance. We collected a dataset of real-world hologram images of oil particles. We then trained our machine learning models on this real-world data with hybrid method derived labels. We used average precision and average recall to compare model performance. Compared to the hybrid method, we demonstrated a processing time reduction of 99\%. YOLOv5 with GPU acceleration proved to be the superior model in all metrics. Machine learning is a promising technique for processing these hologram images rapidly. This allows quick-response agencies such as oil spill response teams the ability to use holographic cameras to assess oil spills.
Subject Area
Computer science|Optics|Environmental science
Recommended Citation
Cruz, Daniel, "Oil Particle Analysis Using Machine Learning and Holography Imaging" (2022). ETD Collection for University of Texas, El Paso. AAI30242379.
https://scholarworks.utep.edu/dissertations/AAI30242379