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heatmaps at runtime is also a useful feature, helping users obtain the location and shape of defects without the need for graphical annotations at training. Teledyne Dalsa is also developing a tiling
feature to allow users to work with larger image sizes and identify smaller defects. Previously, these smaller defects were often reduced in size and lost when they were passed into the neural network.
Time savings By their very nature, AI-assisted image processing tools perform many of the repetitive, low-skill activities within image processing, providing users with more time to focus on value-added or more complex tasks. Te resulting time savings are a key benefit for many users and businesses. When it comes to discussing how AI-
assisted imaging tools actually work, a system such as the Sapera Vision Software offers continual learning – where the deployed AI model learns in the field. In other words, the user does not need to retrain and redeploy the model if any new cases occur after initial training is complete. Te model can continuously adapt, even after it has already been deployed to runtime. As a result, the AI models are able to
train themselves quickly. With a sufficient amount of data, users can get a model up
A licence plate obscured by dirt
and running in as little as a few minutes. ‘Tis is a huge time saver for customers,’
Hunt explained. ‘One of the biggest issues in AI is that when a model is ineffective or new images are presented, then you must retrain the model. Tis replaces any previous work done and does not guarantee an improvement in the results. With continual learning, users can optimise the existing model with new information. Tis both saves a lot of time and keeps any previously saved efforts.’ Time is a critical consideration, not just
when running these AI models but also when labelling the images. Using this type of software, for example,
users can import a folder of images and group them all with a single label. Te semi-supervised object detection (SSOD) functionality allows users to start with a certain number of images, label a few and then the software automatically labels the rest.
New whitepaper now online
Data quality When training any AI system, the quality of the data used is another crucial factor. For example, you may want to train an automatic number plate recognition (ANPR) imaging system to read the plates passing by on a motorway. If the licence plate images are clear and
can be easily identified by a human, then the AI system can also be easily trained. But if there is ambiguity in this data from, for example, faded number plates or those partially obscured by dirt, then there needs to be an agreement between the humans classifying those images. ‘If the humans can’t agree, then the AI model won’t do well,’ Hunt added. In its latest whitepaper Te Importance of
Data Quality When Training AI, Teledyne Dalsa examines this data quality issue in more detail, examining the impact data quality has on the quality of the training for an AI-assisted imaging tool. O
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THE IMPORTANCE OF DATA QUALITY WHEN TRAINING AI
With artificial intelligence (AI) on the rise and making its way more and more into our daily lives, companies are starting to explore what AI has to offer. In its latest whitepaper, Teledyne Dalsa explains why data quality is a key consideration to deploy an accurate AI-based system.
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