Cybercore and SC Automotive Engineering signed a strategic business alliance agreement on customer development and technology development.


Cybercore Co., Ltd., a developer of image recognition AI algorithms (hereinafter referred to as “Cybercore”; Head Office: Morioka City, Iwate Prefecture; President: Hiroshi Abe), and SC Automotive Engineering, Co., Ltd. (“SCAE”; Head Office: Chiyoda-ku, Tokyo; President and CEO: Toshimi Yamanoi), a wholly owned subsidiary of Sumitomo Corporation that provides technology to major automotive companies, have signed a strategic business alliance agreement to provide solutions that utilize Cybercore’s world-leading* image recognition algorithms to customers of SCAE and SCAE’s parent company, Sumitomo Corporation.

SCAE provides a variety of solutions to major automakers and other transportation equipment manufacturers, and this business alliance is expected to leverage Cybercore’s core technologies, including the LuxEye® image sharpening library, the NeuroEye® lightweight AI algorithm, the WipeEye® missing image restoration algorithm Cybercore’s core technologies, and the DetectEye™ AI for judging abnormal products without the need for defective product learning, are expected to be leveraged to help solve customer issues using cutting-edge technologies.

Cybercore has been active in international AI competitions, where few Japanese companies have been successful. In addition, in April this year, the company launched a new AI system for vehicles. In April of this year, the ReID algorithm was ranked number one in the world by the international algorithm evaluation site Papers with Code. In June of this year, they won the YouTube-VOS 2021 Challenge, an international competition for image AI, out of 331 entries, and gave two presentations at the aforementioned CVPR 2021 conference.

In addition to automated driving, we have experience in developing solutions in a wide range of fields, including infrastructure, factory automation (FA), medical and welfare, and retail, such as convenience stores, as well as the RushEye® AI system for measuring congestion rates using depth cameras, which has already been introduced in the Tokyo Metro. We have a track record of developing solutions in a wide range of fields.

SCAE and the Sumitomo Corporation Group will reinforce Cybercore’s strengths in technology and its weaknesses in commercialization and sales expansion and will promote the business alliance with a view to a possible capital alliance in the future. With this opportunity, we expect to further accelerate our efforts to provide society with intrinsically valuable technologies.

Cybercore has released “DetectEye™”, a Zero-false-learning-required, Anomaly Detection AI. The edge version will be available soon.



Cybercore’s train congestion measuring AI system has been broadcasted on NHK WORLD worldwide.


Cybercore’s train congestion measuring AI system developed with Tokyo Metro has been broadcasted on NHK WORLD worldwide with English subtitles. The program was featured on NHK nationwide in Japan in July.

Please watch the story and the movie, so you will understand Cybercore’s vision and culture at a glance.

Cybercore’s train congestion measuring AI system was featured on NHK “Ohayo Nippon”.


Cybercore was featured on NHK (national broadcasting station) for our train congestion measuring AI system this morning. The system was developed collaborating with Tokyo Metro and we have applied patent in March as announced previously.

Please watch the story and the movie, so you will understand Cybercore’s vision and culture at a glance.

Please see NHK website for the article and movie:
Story (Japanese Only)
Movie (Japanese Only)

Update (Aug 30, 2021):
Featured in “Local Digital Transformation (DX)” on NHK WEB:

Read the Story (Japanese Only)

Cybercore R&D team won 1st place in YouTube-VOS 2021 Challenge. The team will make 2 presentations during CVPR 2021, a prestigious Image AI conference.


Researchers from Cybercore Co., Ltd. (Morioka, Iwate) have won the 1st place among 331 entrants and 39 finalists in the international computer vision competition YouTube-VOS 2021 Challenge. The team will have a presentation at one of the most prestigious computer vision conferences CVPR 2021 on June 20th.

Besides the achievement in the YouTube-VOS 2021 Challenge, as previously announced in May, Cybercore team has also won the 1strank in Vehicle Re-Identification in Papers with Code. This algorithm was developed during the AI City Challenge competition The team will present their solution at the CVPR 2021’s AI City Challenge section in June as well.


Overview of YouTube-VOS 2021 Challenge

YouTube-VOS 2021 Challenge has 3 different competitions, and we joined the Video Instance Segmentation one. Our team participated the challenge with 4 researchers from Cybercore’s Ho Chi Minh R&D center.

Video Instance Segmentation is a multi-task problem, which performs three tasks simultaneously, including detection, segmentation, and tracking. Our approach takes the advantage of data efficiency analysis, multi-task learning, and deep-supervision. This not only improves the model’s speed and memory, while yields higher accuracy and reduces training time thanks to the joined optimization framework.

Our solution has a wide range of applications, such as video understanding, object detection and tracking for autonomous driving, human behavior, or motion analysis. Taking the video understanding problem as an example, our solution can segment any objects (person, vehicles, animals) in the videos and track them for specific purposes. Segmented results can serve as another source for action analysis, such as in sport, e.g., supporting coaches with more information to train their teams. In autonomous driving applications, our solution can segment moving objects on the streets, such as pedestrians, riders, other vehicles, or traffic signs. This helps autonomous vehicles be able to take appropriate action on the ride.


CVPR 2021 workshop

YouTube-VOS Challenge Outline and Results



Our train congestion measurement system was featured on DGLAB.


Please refer to here for details.

Article about our train congestion measurement system was published on Iwate Nippo newspaper.


Please refer to here for details.

Cybercore ranked 1st on Papers with Code for Vehicle Re-ID category.


Cybercore’s Vehicle Re-Identification algorithm has been ranked 1st on Papers with Code‘s website.

The algorithm has been developed for AI City Challenge 2021 competition’s Challenge Track 2:City-Scale Multi-Camera Vehicle Re-Identification category. Cybercore performed 9th rank at AI City Challenge and submitted paper to CVPR Workshop 2021, the prestigious computer vision conference coming in June.

Paper Title: “A Strong Baseline for Vehicle Re-Identification”


Related URLs:

Papers with code – Vehicle Re-Identification

Papers with code –

Paper –

CyberCore and Tokyo Metro have applied co-patent for traffic situation assessment system using depth camera and AI.



Please Refer Press Release (Currently only in Japanese):


ISMS (ISO27001) certification was obtained.


On February 18, 2020, ISMS (ISO27001) information security management system certification was obtained.

Please refer to here for details.

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