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.

Features
Fast
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.
Flexible
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.
Accurate
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.

Main Functions
Classification
Quickly determine whether a sample is normal or defectiveDetection
Determine both the type and location of defects (using bounding boxes)Segmentation
Determine both the type and location of defects (pixel-level contours)Image Generation
Generate realistic synthetic images to be used for network training
Specifications
Level
Minimum
Recommended
Operating System
Windows 7, 10, 11 64-bit
CPU
Intel Core i5
Intel Core i7/i9
Environment
Visual Studio 2013
Visual Studio 2017
RAM
16GB
32GB
GPU
NVIDIA GeForce RTX 3060, 3070
NVIDIA GeForce RTX 3080, 3090
STORAGE TYPE
SSD
Key Features
1. Optimal Transfer Learning and Directed Attention Learning
- By designing a transfer learning algorithm that optimally transfers the weights of an existing network, training times are considerably reduced while producing a sparse, easily pruned network
- Our directed attention learning algorithm focuses learning on the more important defective regions

2. Image Data Generation Technology
- The Problem of Insufficient DataDeep learning networks perform only as well as the data with which they are trained. The more the data, and the richer the data, the better the performance. Contrary to expectation, most users have very limited data, especially of defective samples.
- Image Data Generation TechnologyGenerating synthetic data is trickier than it seems: simple manipulations of existing image data, e.g., flipping, rotating, blurring and other distortions, are not helpful. Generating realistic defective images requires more sophisticated techniques.
- GAN-Based Image Data GenerationSaigeVision® makes use of advanced GAN methods developed in-house to generate realistic synthetic data customized for industrial image inspection applications.

Benefits
Improved Inspection Accuracy

Rapid Deployment

Using Image Data Generation for Inspection of a New Product or Line
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Use related product data to train a GAN to generate synthetic images
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Use the trained GAN to generate synthetic data for a new product
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Train the inspection network for a new product using the generated synthetic data images
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Deploy for inspection of a new product
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Generate Realistic and Accurate Synthetic DataSaigeVision® takes advantage of generative adversarial networks (GAN) to generate synthetic images of defects that are virtually indistinguishable from actual defects, even by human experts. A wide range of defect types and sizes can be generated at arbitrary locations while preserving background homogeneity.
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Reliable and Robust GAN TrainingGAN methods are notoriously difficult to use in practice; training a GAN can be time-consuming, and highly sensitive to mode collapse and other instabilities. SaigeVision® uses in-house algorithms optimized for industrial inspection that overcomes these and other challenges, delivering state-of-the-art performance for real scenarios.
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Rapid Deployment for New Products/LinesDeep learning solutions are only as good as the quality and quantity of the data with which they are trained. Using our GAN-generated image data, inspection can be performed even for new products and lines for which data is not yet available.
3. Auto-Labeling
- 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.
