As we move towards the end of 2022, computer vision (CV) technology is touching a market value of $48 billion. In 2023, we can expect to see more innovative trends and developments in this AI-powered technology space. 

CV-powered algorithms enable us to understand both images and videos, thus driving innovations like smarter machinery processes and self-driving vehicles. Among the latest trends, CV technology is used in retail applications for implementing retail heat maps, cashier-less stores, and image recognition. What makes computer vision more powerful is its seamless integration with other enterprise tools like ERP, CRM, and employee attendance.

With its immense potential, here is a look at six computer vision trends we are likely to witness in 2023:

1. Edge Computing

As more data is collected using cameras and sensors, organizations simply don’t have sufficient time to send the data to the cloud. Besides, CV systems are often required to take immediate action on received data notifications. They cannot rely on retrieving data from the cloud.

This is where edge computing becomes viable, for it helps collect and store data in proximity to its source. Cybersecurity and regulations are other important aspects when we talk of integrating CV with edge computing. Businesses must keep tabs on data privacy regulations in association with the way they capture and use video data.

Here is how CV can complement edge computing in 2023:

• Fast and real-time data processing
• Elimination of insecure data centers
• On-demand data analysis for any suspicious activity
• Private architecture for enhanced security

2. Synthetic Training 

Today, AI models can train using synthetic data thanks to advanced GPUs and simulation tools. As such, CV models can now accelerate their self-learning process to respond to real-world settings.

For instance, GPUs can now render high-fidelity data in a few seconds, thus generating massive volumes of training data. Going forward, AI and CV companies will perform research on how to connect the real world with the synthetic world, thus facilitating simulated models to train for real applications. Indeed, going forward, companies must invest in synthetic data and simulation tools.

3. LiDAR Technology

Short for Light Detection and Ranging, LiDAR is another exciting CV trend to look forward to in 2023. This technology is driving the rise of autonomous vehicles and high-resolution maps. As a 3D-enabled technology, LiDAR systems work in tandem with connected cameras to collect real-time visual data (for example, road traffic).

Besides autonomous vehicles, LiDAR has a variety of CV-related applications, including tracking climate change and creating digital elevation models. LiDAR technology can also equip manufacturing robots with efficient mapping and navigation capabilities. 

Using accurate labeling, LiDAR data is more useful for computer vision and supervised machine learning. Going forward, the business challenge is to transform massive LiDAR-generated data into efficient machine-learning models.

4. Explainable AI 

Among the latest trends, explainable AI makes it possible for developers to comprehend a CV model. This is crucial as most AI and machine learning models are “black boxes,” making it challenging to understand how they predict outcomes.

Explainable AI for Computer vision is useful for knowing model predictions and can serve the following purposes:

• Provide pixel attribution methods – useful for CV applications.
• Work with multiple use cases, including object detection & identification, semantic segmentation, and embedding.
• Provide smoothing methods for improving the resolution of computer-assisted models.

5. Workplace Safety

Workplace safety is a growing concern in manufacturing operations. Globally, the workplace safety market is set to increase from $14.2 billion (in 2022) to $26.7 billion (by 2027). Besides their employee safety, manufacturers must ensure optimal machinery conditions and environmental standards. 

In the digital age of Industry 4.0, technologies like AI and image detection can improve productivity and lower operational costs. By combining AI with computer vision, manufacturers can prevent accidents and worker injuries as well as detect real-time potential hazards using high-resolution images. For instance, CV-enabled cameras can capture and analyze real-time images, detect a potential risk, and immediately notify workers and supervisors.

Additionally, intelligent CV algorithms can detect workplace fires, overheating, and more workplace risks in real-time.

6. Updating Data Annotation 

In 2023, the industry’s use of efficient data annotation services is set to expand even more than before. Various sectors like healthcare and automotive are now depending ever more on comprehensive and nuanced data annotation practices. Going forward, automated labeling or annotation can save time and provide labeled datasets quickly for training CV models.

Advanced annotation tools will revolutionize computer vision with techniques like automatic labeling, frame-to-frame object tracking, and smart polygons. Similarly, image annotation tools can enable CV systems to acquire a high-level understanding of digital images or videos.

Trained AI models can also recognize predetermined features in non-annotated images and videos. Image datasets like MS Coco Dataset and the Google Open Images Dataset (OID) are now providing pre-annotated images useful for AI and CV applications.

Conclusion

Computer Vision applications are advancing to wider use cases across industry domains. CV solutions play a decisive role in cybersecurity, retail applications, and safety practices for modern manufacturing.

As an experienced AI solution provider, KamerAI can integrate its CV capabilities with third-party enterprise solutions, including ERP and CRM. We are powering the next wave of innovation in the computer vision domain.

If you want to know how our CV solutions can empower your business, get in touch.