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Tuesday, October 15 • 11:30am - 11:45am
Object Detection with Deep Learning in the Oil and Gas Industry

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Oil and gas companies are used to analyze different types of 2D images. For example, to date rocks, many microfossils are identified and counted on large microscope images. This research is applied to microfossil detection and could benefit the other use-cases.

To automate this cumbersome work, we suggest a hybrid system consisting of two steps: 1) a heuristic over-segmentation to localize regions of interests (ROIs) with traditional computer vision; 2) a Convolutional Neural Network (CNN) trained to classify ROIs. This hybrid system presents two advantages compared to the state-of-art approach of object detection like those applied to IMAGENET. First, data management of supervised CNN classifiers is more flexible because they are trained on ROIs and not on the overall input image. Second, researchers have focused more on CNN classifiers because of their simplicity. 

Finally, we study the quality of the detection of this system on our micro-fossils detection application.


Tuesday October 15, 2019 11:30am - 11:45am CDT
BRC 103

Attendees (3)