Hacarus’ releases their AI based Visual Inspection Solution as beta for trial – Hacarus Inc.
A modular solution targeted as secondary inspection step – integrated with or alongside existing inspection systems.
Hacarus Inc.(Headquarters: Kyoto-shi, Nakagyo-ku, CEO: Kenshin Fujiwara) announces the launch of a beta trial of their visual inspection solution that is based on Artificial Intelligence (AI).
Hacarus has created an AI that applies sparse modeling technology, which excels in feature extraction from small amounts of data. The team at Hacarus has prepared dedicated AI models to cover various inspection cases with high performance.
The beta version is available for trial for companies interested in this solution from April 1st, 2019 until July 2019.
Existing visual inspection systems already achieve high inspection performance, but a common problem is over-detections, so-called false-positives. A secondary manual inspection of these products by trained inspection staff is required.
Hacarus’ visual inspection solution is tailored to automate this secondary inspection and to reduce the amount of manual work.
Various types of inspection objects are already supported, such as circuit boards, precision parts, metal, plastic, food, and so on – new models can usually be easily created with a small amount of training data.
Hacarus’ visual inspection solution can be used as standalone application or integrated into existing visual inspection systems as a module. By using our API and SDK, it is easy to integrate our AI with an existing inspection line.
When used as a single application, it provides a dashboard for administrators and an operation panel for production line workers, that can be operated through a tablet or touch panel.
Established in 2014 as a healthcare related AI venture, Hacarus develops its own AI through the application of sparse modeling technology. We build modular solutions for various industrial and medical use cases. Our technology excels in extracting features from small amounts of data and can be applied in uses cases that are difficult to solve with deep learning.