Performing consistent inspections throughout the manufacturing process ensures higher product quality. Employers previously had to invest significant resources in these repetitive inspections. However, thanks to the computational capabilities within sensors in machine vision, inspections can now be completed faster and more conveniently.
Implementing machine vision networks is crucial for minimizing redundant data exchanged during processing and inspection. In this blog post, I'll explore the concept of in-sensor computation, which involves offloading computational activities to sensory endpoints, and how this phenomenon can be applied to machine vision and cameras.
I. What is in-sensor computing?
Cameras and processors still differ in machine vision. However, as integrators discover new uses for machine vision systems, they encounter greater challenges, one of which is space. Machine vision workstations cannot be placed in every location. Furthermore, smaller systems have historically struggled due to a lack of available computing power. To address this challenge, researchers have begun exploring methods to improve the speed of machine vision by integrating computation within the image sensor. This is called in-sensor computation.
II. What is a machine vision sensor?
Before delving into in-sensor computation, let's explore machine vision and its implications for automation systems. In the early implementations of robotics in manufacturing, simple programs could automate simple, repetitive tasks. This process was effective for producing identical components with minimal or no variation.
However, the complexity of modern manufacturing necessitates equally sophisticated factory automation systems. To adapt to this more complex reality, technologies such as in-sensor computing offer automation systems greater flexibility and the ability to dynamically adapt to different components. In fact, machine vision computing can help cameras handle a greater degree of processing during their evolution, providing additional sensing capabilities. Cameras will be able to make decisions based on large datasets, eliminating most detection errors and improving the safety and quality of consumer products.
III. How can in-sensor computing advance machine vision?
This technology avoids the step of transmitting the entire image to machine vision software; instead, all processing is done by the sensor's computing power within the camera. This reduces the bandwidth utilization required for a constant high-resolution image moving between different components, thus reducing the processing time required by the vision system.
This increased efficiency reduces the need for expensive, interdependent systems in applications. If a camera's sensor can identify what it sees while analyzing an image, decisions can be made without relying on cloud connectivity. Self-processing sensors could open up new opportunities for machine vision in autonomous vehicles and other digital business applications.
IV. Exploring the Possibilities of the Future
Considering energy and latency budgets, current hardware limits the potential of new AI-based image processing applications. Over 90% of sensor-generated data is copied and processed, wasting time and effort. Technological innovations in sensors for machine vision include creating new, compact material systems to handle sensing and processing. The ultimate goal of in-sensor computing is to create programmable, high-resolution, and fast, efficient AI hardware.
