No-Code AI training tool to quickly deploy AI models for machine vision solutions.
Teledyne DALSA Astrocyte empowers users to harness their own images of products, samples, and defects to train neural networks to perform a variety of tasks such as anomaly detection, classification, object detection, segmentation, and noise reduction. With its highly flexible graphical user interface, Astrocyte allows visualizing and interpreting models for performance/accuracy as well as exporting these models to files that are ready for runtime in Teledyne DALSA Sapera and Sherlock vision software platforms.
Astrocyte significantly improves quality, productivity, and efficiency in X-ray medical imaging
The tiny and random nature of fibers on X-ray detectors make them challenging and time consuming for traditional methods or humans.
With Astrocyte, the X-ray solutions team was able to rapidly identify all defects and even outperform their operators.
“Article Originally published in Novus Light.”
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Surface inspection on metal plates
Classification of good and bad metal sheets. Tiny scratches on metal are detected and classified as bad samples. Astrocyte detects small defects on high resolution images of rough texture. Just a few tens of samples are required to train a good accuracy model. Classification is used when good and bad samples are available, while Anomaly Detection is used when only good samples are available.
Location/identification of wood knots
Localization and classification of various types of knots in wood planks. Astrocyte can robustly locate and classify small knots 10-pixels wide in high-resolution images of 2800 x 1024 using the tiling mechanism which preserves native resolution.
Detection/segmentation of vehicles
Detection and segmentation of various types of vehicles in outdoor scenes. Astrocyte provides output shapes where each pixel is assigned a class. Usage of blob tool on the segmentation output allows performing shape analysis on the vehicles.
Noise reduction on x-ray medical images
Denoising of high-noise x-ray medical images such as dental and mammography. Astrocyte provides good output signal-to-noise ratio while preserving image sharpness.
Astrocyte supports the following deep learning architectures.
Classification involves predicting which class an item belongs to. Some classifiers are binary resulting in a yes/no decision. Others are multi-class and can categorize an item into one of several categories. Classification is used to solve problems like defect identification, character recognition, presence detection, food sorting, etc. Astrocyte supports the following classification neural networks: Resnet-18, Resnet-50, Resnet-101. Astrocyte also supports continual classification allowing further training at inference time.
Anomaly Detection is a binary classifier dedicated to identifying good and bad samples. Unlike regular classification, Anomaly Detection can train on unbalanced datasets (i.e. large number of good samples and small number of bad samples). Anomaly Detection is used on any application involving identification of defects on a surface or scene. Anomaly Detection produces heatmaps at runtime which are useful for finding the location and shape of defects. Astrocyte supports the following anomaly detection neural networks: Alexnet, VGG16 and Resnet-18.
Object Detection involves localizing one or more objects of interest in an image. It combines the two tasks of localizing and classifying objects into one single execution. The output of Object Detection includes bounding box and a class label for each of the objects of interest. Object Detection is used to solve problems like presence detection, object tracking, defect localization and sorting, etc. Astrocyte supports the following object detection neural networks: SSD300, SSD512, SSDLite and YOLOX.
Image segmentation involves dividing input image into segments to simplify image analysis. Segments represent objects or parts of objects and are composed of groups of pixels. Image segmentation sorts pixels into larger components
eliminating the need to consider individual pixels as units of observation. Image segmentation is a critical process in computer vision and is used for defect sorting/qualification, food sorting, shape analysis, etc. Astrocyte supports the following segmentation neural networks: DeepLabV3-Resnet-50, DeepLabV3-Resnet-101, Unet.
Image denoising aims to reconstruct a high-quality image from its degraded observation. It represents an important building block in real applications such as digital photography, medical image analysis, remote sensing, surveillance and digital entertainment. Astrocyte supports the following noise reduction neural networks: Residual Channel Attention Network (RCAN).
Generating image samples
Importing image samples
Importing/creating annotations (ground truth)
Visualizing/editing dataset
| Operating System | Windows 10 64-bit |
| GPU | Minimum recommended: NVIDIA GeForce GTX 1070 with 8GB RAM or equivalent. |
Graphical interfaces in Sapera and Astrocyte software make it easier to implement your own deep learning network. Read more in this article from our Possibility Hub: From Training to Inference: Creating a neural network for image recognition