ARTIFICIAL INTELLIGENCE
Translating user experience into AI edge imaging
An iterative approach is needed to build a real-world AI vision application on embedded hardware, say Irida Labs’ Vassilis Tsagaris and Dimitris Kastaniotis
D
eep learning models have been proven to achieve human levels
of performance in many tasks and are nowadays the backbone of computer vision products. Tese models are very good at solving well-defined tasks with enough data, but in real-world applications it’s very unlikely that a task will have been defined from scratch. Additionally, the
computational capacity is bounded by hardware cost and power consumption, critical factors for mass deployment of products. Finally, decisions made by deep learning models are hard to interpret, and there is no mechanism to translate user experience into rules that would improve the model’s decisions according to the customer’s needs. Computer vision is a powerful
tool for products and services for daily life. Most of these applications are deployed on edge devices under the Internet of Tings umbrella. However, IoT concepts are
mostly designed to support the deployment of fixed functionalities like sensors, but not computer vision algorithms, which by default are entities that need to evolve over their lifetime. In order to close the gap
between writing computer vision algorithms and deploying them on IoT devices, Irida Labs has developed an approach where user experience helps shape the end-product, including model performance and hardware selection.
User experience A key concept of the Irida Labs approach is to rely on an agile development process that allows the customer to modify or adapt the definition of the task. Indeed, in most computer vision products, the customer tries to summarise the task in very abstract ways, like ‘people detection’. Tis description is usually
followed by a small test set, which is not representative of the underlying data distribution
22 IMAGING AND MACHINE VISION EUROPE FEBRUARY/MARCH 2020
‘Working with highly pruned machine learning models that operate with low- bit accuracy is the selection of choice’
of a real-world deployment. Tis is a weak definition of the task as it does not consider factors like the viewing angle, lighting, whether it’s deployed indoors or outside, and weather conditions. In order to overcome this
problem, the deployment of the device is connected to model development and, in particular, to data management and user experience. Te main goal of this approach is to translate user experience into a representative test set. Tis is implemented by a software mechanism for receiving user feedback. Te feedback comes from the real-world deployment of the
first version of the model and allows the model to learn what samples are important. In this manner, Irida Labs provides a deep learning model that’s a good starting point, and then, together with the customer, iterates feedback to improve the model.
Data management and model development Te first version of the computer vision model uses data that matches the description of the task and the first test set of data. Subsequent releases, however, will require more application- specific data – the true challenge is in collecting the appropriate data.
Te process of gathering
data involves a number of mechanisms that can affect the model’s performance. For example, data sampling might introduce biases that are hard to detect. To overcome these pitfalls, Irida Labs uses a development process that incorporates some kind of intelligence and minimises
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