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Construction & Infrastructure


Automation of rebar reinforcement inspection using image processing

Collaborative work with Mitsui Consultants Co., Ltd

With Mitsui Consultants Co., Ltd., we have tried to automate the inspection of reinforcement used in concrete structures using image processing.

When manufacturing concrete structures, it is necessary to check during the manufacturing process whether the reinforcing bars placed in the concrete are correctly assembled, but until now this inspection has been very time-consuming and labor-intensive. We participated in a demonstration project with Mitsui Consultants Co., Ltd, where we were responsible for automating the inspection process using image processing.

We have built an image processing pipeline that automatically measures the diameter of rebar, the distance between rebar, and the length of overlapping areas, and have succeeded in measuring with relatively small errors, especially for the distance between rebar and the length of overlapping areas.

In the future, this technology is expected to contribute to significant labor savings at construction sites by being used for the inspection of reinforcing bars of various types.

Discovering cracks from the outer wall of a dam using deep learning

Collaborative work with Dr. Pang-jo Chun, University of Tokyo

We have participated in a project to detect cracks from the photos of the concrete walls of dams in Japan, under the supervision of Project Associate Professor Pang-jo Chun of the University of Tokyo i-Construction System Studies endowed chairs.

There are two types of scratches and cracks that can be observed on the exterior of a dam: the type of cracks that are not very problematic for the long-term maintenance of the dam, and those that can lead to structural problems in the concrete exterior. In this project, we used deep learning to simultaneously detect cracks that were diagnosed by expert civil engineers as likely to lead to problems, and to exclude less critical cracks from detection.

We have built a pipeline that can switch between various image segmentation techniques, and have achieved a level of AI system implementation that can withstand actual operation.

The system is also capable of indicating where cracks exist in a large image, even in orthoimages taken by drones, as the deep learning model performs inference on the entire image.

Since this system can be easily relearned using additional data, it is expected that its performance will be greatly improved sequentially as more data sets are provided in the future.

Healthcare and medical research


Development of BrainSuite, a program for brain checkups to visualize brain health level and dementia risk

Collaborative work with CogSmart, Inc.

As a technology partner, we have participated in the development of BrainSuite, a groundbreaking program for brain checkups that visualizes brain health levels and dementia risks using machine learning algorithms.

I was in charge of integrating the algorithm devised at Tohoku University's Institute of Development, Aging and Cancer into the application, connecting the MRI image handling part and cognitive function test, and implementing the basic part of the online interview.

We also facilitate meetings in both English and Japanese in an international team that includes foreign engineers and researchers. In addition, through a long-term project, I was involved in the overall design of the application, including cyber security, and the team also responded to reviews from medical institutions during development as needed.

BrainSuite is sold by CogSmart to various medical institutions in partnership with Philips Japan. In the future, we are confident that unprecedented brain health checkups using AI will help improve brain health and prevent dementia in more people.

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