Computer vision technology enables a variety of applications across industries. It allows factories to monitor equipment and raise alarms for maintenance or quality issues. It helps retailers track product performance and customer sentiment in real time to optimize advertising.

More advanced vision technology trends include generative AI that creates original content and 3D models for more precise and in-depth visualizations. We also see advancements in hardware that reduce processing time and power consumption for embedded vision systems. Learn more by visiting our Website.

Event-Based Sensing

The most fundamental shift in vision technology is event-based sensing, mimicking how the eye and brain process images to overcome the barriers of conventional frame-based machine vision. This approach radically improves performance in many applications, particularly those that involve motion detection and tracking.

Conventional vision systems work by recording the same image or video over and over again at each time increment, which is grossly inefficient for some industrial use cases. It oversamples the part of the scene that isn’t changing and under-samples the areas where action is taking place. This results in a waste of 90% of the data required to process an image.

Event-based sensors solve this problem by only recording what has changed, allowing them to deliver far more information much faster with significantly less latency, bandwidth demand, storage requirements, and power consumption. A company has developed neuromorphic sensors and bio-inspired algorithms that function like the human eye and brain.

They work by generating events that correspond to changes in the brightness of individual pixels. These events are then used to compute the pixel’s motion. In addition, the system determines whether each change is caused by a moving object or by the camera’s ego motion (which causes frame blur).

Using this approach, Prophesee sensors capture up to 1,000 times more information per pixel than conventional image sensors in the same space, which makes them ideal for motion detection and tracking. This also reduces the computational load on the sensor, enabling it to run on less powerful processors and to perform reliably in the presence of noise and interference.

In a new development, the company is teaming up with another company to produce two low-power, high-performance event-based sensors. These sensors utilize a combination of an image sensor and an event-based Metavision technology. They offer the industry’s lowest pixel size of 4.86 m and have built-in H/W event filters to eliminate unnecessary event data such as periodical events due to light flickering and other events that are unlikely to be caused by moving objects.

With the right software and hardware, these new sensors can be used for a variety of applications including industrial inspection, security, surveillance, smart traffic management, and automotive driver monitoring systems (DMS). In 2024, these technologies are expected to drive significant advances in several sectors.

Deep Learning

Deep Learning (DL) is a subset of machine learning that uses neural networks with multiple layers to acquire expert-level feature representations. This technology can be used across a wide range of industries and applications to perform tasks like image recognition, natural language processing, dimensionality reduction, pixel restoration, and more. DL algorithms can be trained with structured and unstructured data, allowing them to identify patterns in data that may not be apparent with other methods.

For example, a deep learning-based text generation model can take a piece of text and automatically generate a new text that matches its proper grammar and style. This can improve customer experience and make businesses more efficient. Another application of DL is in medical imaging, where it can analyze complex biological data and speed up research, leading to more effective treatments and cures.

In industrial automation, DL can help streamline processes and increase productivity by automating repetitive tasks that would otherwise be difficult or impossible to do manually. In addition, DL can be used to detect faults in products, which can save time and money by eliminating the need for manual inspection.

Other DL applications include image classification, object detection, and semantic segmentation. Using this technology, computers can determine which pixels belong to an object, such as roofs or cars, and then draw a bounding box around the object. In more sophisticated use cases, DL can also distinguish the unique features of each object and assign them to their category.

These technologies can be used to enable a wide range of applications, including self-driving cars, voice-activated assistants, and virtual reality. They can also be used in healthcare to diagnose diseases and improve treatment, as well as in aerospace and military applications for remote sensing and spotting threats.

While these advances in computer vision can have a profound impact on our everyday lives, it is important to remember that they come with ethical implications. As with any new technology, we must ensure that we are incorporating it in a way that will benefit society and avoid introducing bias or unfairness.

Edge Computing

Computer vision technology is used to recognize and interpret image data, enabling automated processing and understanding. This is often achieved through machine learning models, such as deep learning and convolutional neural networks. These models are trained on large datasets, which helps them learn how to identify and understand various patterns and features. When the system encounters a new image, it compares it to these previously learned patterns to analyze and interpret the scene.

In the future, we will see more applications of vision systems in the workplace. For example, in manufacturing, computer vision systems can detect defects in products to optimize production processes and ensure quality control. In retail, they can help retailers manage inventory and provide a seamless shopping experience for customers. Similarly, in medical imaging, computer vision can help streamline practices for radiologists by detecting skin cancers or other diseases.

With the proliferation of voice assistants and other devices that use visual feedback, computer vision is expanding into new fields, including human-machine collaboration. This trend is driven by the desire to design systems that work in tandem with humans rather than replace them.

Vision systems are a key component of these technologies, and there are several trends we can expect to see in 2023. For one, we will see a move toward embedded vision, which involves integrating the technology into sensor-laden devices. This allows the devices to process visual data locally, which reduces network latency and privacy concerns. It also enables vision systems to respond to environmental changes quickly and accurately.

Additionally, we will see increased use of 3D modeling, a technique that creates digital models of real-world locations or objects to allow for more precise and in-depth visualizations. This is especially important for virtual reality (VR), where it can be used to enhance immersive experiences. Finally, we will see a shift toward self-supervised vision, which is the ability of a system to learn and understand visual content without extensive labeled data. This can reduce the need for high-cost, slow computing resources and speed up data analysis. It can also make the results of a vision system more transparent and interpretable, which is critical in fields such as medicine where errors made by machines could have life-or-death consequences.

Embedded Vision

In the Internet of Things (IoT), embedded vision can be found in a wide range of sensors that are designed to communicate with each other. These sensors often have built-in image processing capability and, therefore, can perform advanced functions like object detection and recognition.

As a result, these systems can be used to support a wide variety of applications. Examples include augmented reality (AR), robotics, autonomous vehicles, and medical imaging and diagnostics.

Embedded vision systems are typically smaller than PC-based vision systems, and they can be easily integrated into existing devices. They also tend to use less energy and feature lean designs. These benefits make embedded vision ideal for applications where space and/or power are limited.

The advantage of embedded vision is that rather than sending images from a sensor to a central processor, initial analysis is performed adjacent to the sensor on a dedicated, application-specific processor. This greatly reduces data transmission requirements and enables faster system responses.

Panelists agreed that embedded vision systems offer many advantages over traditional PC-based systems. However, they pointed out that a few areas need improvement to encourage wider adoption. The first is cost. Many applications that would benefit from vision detection aren’t currently justified because of the high cost associated with this technology. Panelists hope that lower prices will make these technologies more accessible and will open up new possibilities.

Another area that needs improvement is ease of use. While there are several benefits to using embedded vision, panelists noted that it can be difficult to integrate this technology into existing machinery without a lot of prior experience or knowledge. They also noted that several applications require specialized hardware. Panelists hope that this will be easier to do in the future with more streamlined software and hardware solutions, including pre-made kits that provide a quick and easy start.

In the future, embedded vision will be even more important as it becomes more commonplace in devices. For example, augmented reality will depend on embedded vision systems to accurately identify and map the world around us. Embedded systems will help make AR more immersive and intuitive. They will also be needed to improve the accuracy of autonomous vehicles and other types of SLAM (simultaneous localization and mapping) systems, which are being used in place of GPS.