Application of environmental CV technology in new generation IoT systems

Today, computer vision (CV) technology is at an inflection point, with major trends converging to make cloud technology ubiquitous in tiny edge AI devices optimized for specific uses , these devices are usually battery powered.

Advances in technology address specific challenges that enable these devices to perform complex functions natively in constrained environments, including size, power and memory. This cloud-centric AI technology is extending to the edge, and new developments will make AI vision at the edge ubiquitous

Understand the technology

CV technology is indeed at the edge, and is Implement the next level of human machine interface (HMI).

Context-aware devices can not only sense the user, but also the environment in which the user is located, allowing them to make better decisions and achieve more useful automated interactions.

For example, a smartphone can visually sense a user's attention and adjust its behavior and power strategy accordingly. This is useful for saving power (turning off the device when no user is detected), improving security (detecting unauthorized users or unwanted "lurkers"), and provides a smoother user experience. In fact, by tracking the gaze of bystanders (bystander detection), the technology can further alert the user and hide the screen content until the user is unobstructed

Another example: Smart TVs can sense whether someone is watching and where to watch it from, then adjust the image quality and sound accordingly. It can automatically shut down to save power when no one is present. Air conditioning systems optimize power and airflow based on room occupancy to save energy costs.

The emergence of home offices and hybrid working models has made instances of smart energy utilization in buildings and other instances more financially important

The applications of this technology are not limited to televisions and personal Computers also play a vital role in manufacturing and other industrial fields. For example, in terms of safety supervision, it can be used for object detection, predictive maintenance and manufacturing process control, such as the enforcement of restricted areas, safe passages and protective equipment. Agriculture is also another area that can benefit from vision-based situational awareness technology, such as crop inspection and quality monitoring

Applications of Computer Vision

Advances in deep learning have enabled many amazing Things are possible in computer vision. Many people don’t even know how they use computer vision technology in their daily lives. For example:

• Image classification and object detection: Object detection combines classification and localization to identify objects in an image or video and specify their location in the image. It applies classification to different objects and uses bounding boxes. CV works through mobile phones and can be used to identify objects in images or videos.

Banking Industry: CV is widely used in areas such as fraud control, identity verification, data extraction, etc. to enhance customer experience, enhance security and improve operational efficiency

Retail Industry: By developing computers Vision systems are used to process this data, making digital transformation of real industries more accessible, such as self-checkouts

Self-driving cars: Computer vision is used in detecting and classifying objects (such as road signs or traffic lights) and creating 3D maps. Or play a key role in motion estimation, thereby realizing the realization of self-driving cars

Edge CV

Visual processing based on machine learning has an obvious trend in the edge field. Hardware costs continue to fall, computing power increases significantly, and new methods require less power and memory to train and deploy small-scale models. These factors have lowered the barriers to adoption of edge AI technology and facilitated its use. But even as we see more and more ubiquitous micro-AI, there is still work to be done. To make ambient computing a reality, we need to serve long-tail use cases in many market segments, which can create scalability challenges.

In consumer products, factories, agriculture, retail and other fields, each new task requires a different algorithm and a unique data set to train on. Solution providers offer more development tools and resources to create optimized ML-enabled systems to meet specific use case needs.

TinyML

TinyML is a key enabler of all types of artificial intelligence at the edge. This is an approach to developing lightweight and energy-efficient ML models directly on edge devices by leveraging compact model architectures and optimized algorithms.

TinyML enables AI processing to occur locally on the device, reducing the need for constant cloud connectivity. In addition to lower power consumption, the TinyML implementation reduces latency, enhances privacy and security, and reduces bandwidth requirements.

Additionally, this enables edge devices to make real-time decisions without over-reliance on cloud infrastructure, making AI more accessible and practical in a variety of applications, including smart devices, wearables Equipment and industrial automation. This helps address feature gaps and enables AI companies to upgrade software around their NPU products by developing a rich set of model examples - a "model zoo" - and application reference code

In this way, they can optimize the appropriate algorithms for the target hardware to solve specific business needs within determined cost, size and power consumption constraints, thus supporting a wider range of long-tail applications while ensuring design success.

The above is the detailed content of Application of environmental CV technology in new generation IoT systems. For more information, please follow other related articles on the PHP Chinese website!