Image Inspection Toolkit
Automate your vision inspection tasks with the powerful, reliable, and easy-to-use SaigeVision® platform. Employing the state-of-the-art in machine learning algorithms, with advanced tools for data generation, augmentation, and labelling, SaigeVision® offers unmatched accuracy and speed for the most challenging inspection problems. Even with only limited training data, SaigeVision® achieves maximum performance with minimum operational overhead.
Using a combination of optimal transfer learning to construct sparse networks, advanced model compression and pruning techniques, and code and algorithm optimization, SaigeVision® achieves best-in-class inspection speeds. Training times are also greatly reduced via focused attention learning.
SaigeVision® is designed to be intuitive, modular, scalable, and easily integrated and deployed. Various labelling and support tools considerably ease the user’s burden in preparing and training the network. No expertise in rule-based vision systems and programming is required.
SaigeVision® achieves best-in-class accuracy through a combination of our proprietary image generation technology, auto-labelling, and other advanced machine learning techniques. Both false negatives and false positives are significantly reduced.
ClassificationQuickly determine whether a sample is normal or defective
DetectionDetermine both the type and location of defects (using bounding boxes)
SegmentationDetermine both the type and location of defects (pixel-level contours)
Image GenerationGenerate realistic synthetic images to be used for network training
Windows 7, 10, 11 64-bit
Intel Core i5
Intel Core i7/i9
Visual Studio 2013
Visual Studio 2017
NVIDIA GeForce RTX 3060, 3070
NVIDIA GeForce RTX 3080, 3090
1. Optimal Transfer Learning and Directed Attention Learning
2. Image Data Generation Technology
Improved Inspection Accuracy*False Positive: A defect incorrectly identified as being non-defective
Using Image Data Generation for Inspection of a New Product or Line
- For most industrial inspection tasks, the data with which the deep learning network is trained is labelled by human experts. This can be time-consuming, and humans are prone to fatigue and misjudgment. Since most of the training data is from non-defective samples, auto-labelling can be used to identify those images that are non-defective, separating these from the much smaller number of images that are likely to contain defects. Human experts then only need to examine and label this much smaller set of images.