Projects

We at Saiwa Inc. are thrilled to announce the successful completion of an innovative project focused on the measurement of interpupillary distance (IPD) and pupil height. This groundbreaking solution was developed in collaboration with Innovation Venture Farm to address the growing need for precision in optical device fitting and design. By leveraging cutting-edge AI techniques and the Mediapipe framework, our solution detects and calculates critical parameters such as the center of the pupil and the lowest point of glasses lenses. These measurements are crucial for applications in eyeglasses, VR/AR headsets, and other optical technologies. The results are delivered with unmatched accuracy and efficiency, empowering manufacturers to deliver tailored products to their users. This project also integrates Saiwa’s Annotation Services, which ensure data labeling precision and scalability, making it easier for users to achieve reliable outcomes.

Powered by Sairone and funded by OAFRI, this project leverages AI and drone imagery to detect herbicide-tolerant Canada Fleabane in Ontario soybean fields. Designed to empower farmers with no-code tools, it transforms raw agricultural images into actionable insights—enabling geotagged weed detection, herbicide mapping, and data ownership. By lowering tech adoption barriers, this initiative supports sustainable, privacy-first farming across Canada.

Discover how Saiwa Inc. leveraged cutting-edge AI and deep learning to detect surface defects on both white and black paper with high precision. This project revolutionized quality control for a leading paper manufacturer—improving accuracy, reducing waste, and enabling real-time defect insights.

To enhance Ducks Unlimited Canada (DUC)‘s capability for UAV-based surveillance of European Water Chestnut (EWC) through machine learning, we at Saiwa have previously implemented the initial version of the EWC detector software. In the second stage, we are in the process of finalizing the product’s features and upgrading its interfaces.The two primary features to be incorporated in this stage are as follows: Incremental learning for gradually training the deep network over time. This feature enables us to rectify false positive and false negative detections over time. Reporting the 3D universal coordinates of EWC locations using drone configuration and temporal GPS data.