The processing flow for automated visual inspection.
The main requirement for AVI is the software layer, which at its core is the computer vision technology that helps inspect products or any object of interest for defects and absence/presence of certain parts.
The software part of an automated visual inspection system requires advanced image analysis algorithms and heavy programming. These algorithms process images to adjust their quality, locate interesting points and regions, and, technology glasses finally decide based on the features found in these regions.
Deep learning technologies have enabled automated visual inspection systems that outperform human or traditional machine vision processes
Deep learning models have proved to be indispensable part of the software owing to the huge success that they have shown in solving inspection problems. They can be trained on thousands of images of, say, bolts, a deep-learning algorithm gradually learns to detect any meaningful deviations from the “standard” appearance of a bolt. Depending on your use case, your inspection problem could be solved using one or a combination of different tasks like Object Detection, Semantic Segmentation and Image Classification. It could also involve OCR models to read serial numbers or barcodes.
how to decide on automated visual inspection system
In order to maintain high image processing speeds, a trained deep learning model has to be generally deployed on high resource computers. For instance, a GPU is necessary to get results in real time.
Cost of automation – robot prices and labor compensation in manufacturing
Several factors play a role in the resultant accuracy and performance of the inspection model – lighting conditions, the number of products to inspect, types of defects to look for, size of the defects/objects, the resolution of the image to name a few. An automated visual inspection system thus requires a team of skilled R&D engineers capable of building such complex systems.
Each manufacturing unit has different and often unique data(images) – these could be because of the a different camera type, indoor illumination or the product itself. Therefore, the software part of AVI, is always a custom solution tailored to the specific inspection needs.
Nanonets to the Rescue
Nanonets helps you build and deploy deep learning models that are essential to develop the software to automate visual inspection
Experimenting with a new unexplored domain in order to improve business requires an efficient team with years of experience and a proven track record of building deep learning solutions for businesses.
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There are many more factors to consider when training deep learning models, such as how you’re going to preprocess your data, define your model, and actually get a computer powerful enough to run the model.
Nanonets provides easy to use APIs to train and deploy deep learning models that are custom tailored to the specific needs of a business. It takes care of all of the heavy lifting needed to build a production ready high accuracy model – data augmentation, transfer learning and yes, hyperparameter optimization!