PFN releases deep learning-based, high-precision, visual inspection software
Oct. 11, 2018, Tokyo Japan---
Preferred Networks, Inc. (PFN, Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa) has developed Preferred Networks Visual Inspection, high-precision, visual inspection software based on deep learning technology. PFN will start licensing the software to partner companies in December 2018. We will announce the new software at a new product seminar (N3-5) on Thursday Oct.18 during the CEATEC Japan 2018 exhibition held in Makuhari Messe near Tokyo.
The use of machine learning and deep learning technologies is spreading rapidly in many areas including the manufacturing floor. However, existing visual inspection systems based on deep learning require as many as several thousand images for training, as well as engineers to annotate the considerable number of images to facilitate the training process. Poorly explained inspection results are also among other issues that have been tackled.
In order to solve these problems, PFN has utilized its technical know-how acquired through the development of the deep learning framework ChainerTM and applications of deep learning to our main business domains - transportation systems, manufacturing, and bio-healthcare - to develop the Preferred Networks Visual Inspection.
●The main features of Preferred Networks Visual Inspection:
1.An inspection line can be set up with a small amount of training data (as few as 100 images of normal products and 20 images of defective products)
2.Plastic, metal, cloth, food, and other materials with various shapes can be handled
3.Results are well-explained through visualized anomalies such as scratches, foreign objects, and stains
4.Training is made easy even for non-engineers with intuitive user interfaces
Preferred Networks Visual Inspection consists of a training support tool and CPU-based defect detection software. Depending on requirements, our licensed partners will install a combination of system components which include training workstations, inspection PCs, photographing equipment, UIs for visualization and operation. GPU-based, fast detection software is also available as an option.
The new product will enable users to build an easy-to-use and highly reliable auto-inspection system at a low cost in a short period of time. This product can be introduced with ease to the manufacturing lines which have been difficult to automate by existing products due to their high costs and inflexibility. In addition, defects are visualized so that its results can be easily explained. This is useful for passing down inspection skills and sharing knowledge with others in the company.
●New product announcement
PFN will announce Preferred Networks Visual Inspection at a New Technologies and Products Seminar (N3-5) entitled “Visual inspection system and picking robot solution based on deep learning” at CEATEC Japan in Makuhari Messe.
● Date & Time: 12:30-13:30 Thursday Oct. 18, 2018
● Venue: Room B (Exhibition Hall 4) in Makuhari Messe, Chiba
PFN will continue to promote practical applications of machine learning and deep learning technologies in the real world.
◆About Preferred Networks, Inc.
Founded in March 2014 with the aim of promoting business utilization of deep learning technology focused on IoT, PFN advocates Edge Heavy Computing as a way to handle the enormous amounts of data generated by devices in a distributed and collaborative manner at the edge of the network, driving innovation in three priority business areas: transportation, manufacturing and bio/healthcare. PFN develops and provides Chainer, an open source deep learning framework. PFN promotes advanced initiatives by collaborating with world leading organizations, such as Toyota Motor Corporation, Fanuc Corporation and the National Cancer Center of Japan. (https://www.preferred-networks.jp/en/)
*Company names and product names written in this release are the trademarks or the registered trademarks of each company.
PFN will announce Preferred Networks Visual Inspection at a New Technologies and Products Seminar (N3-5) .