Smart Cities

Retailers can use an edge AI appliance such as Lanner’s EAI-I130 instead of relying on cloud computing. The appliance connects to these cameras’ data feeds and provides AI and CV at the edge.

Communities across the globe are grappling with the consequences of climate change, ranging from devastating floods to wildfires.

Continuous surging growth in urban populations creates a demand for urban mobility, a greater demand for faster traffic flow, improved public safety, and more innovative transportation.

Light rail drivers will inevitably face challenges — a car suddenly brakes in front, an animal crosses by, or a bicycle runs on the tracks

Ensuring the safety of students and staff in schools has become increasingly challenging. Despite the presence of surveillance cameras, the sheer volume of footage makes it impractical for staff to manually review all video to identify incidents or suspicious activities.

Security screening at airports, border checkpoints, and large public venues demands both speed and precision.

Aquaculture operations face significant challenges in monitoring fish growth and welfare. Traditional manual inspections are labor-intensive, time-consuming, and prone to human error.

Retailers need smarter, faster, and more natural in-store engagement. An offline AI concierge delivers human-like conversation, real-time product knowledge, and instant responses—reducing latency, cutting costs, and protecting customer privacy.

Rail Obstacle Detection refers to the process of alerting railway operators about obstacles or obstructions present on the railway tracks.

In conventional retail settings, manual checkout processes often lead to long queues, resulting in reduced overall store efficiency and frustrated customers experiencing decreased satisfaction.

Ensuring the safety of students and staff in schools has become increasingly challenging. Despite the presence of surveillance cameras

Urban mobility is increasingly strained by congestion, traffic violations, and inefficient parking—challenges that demand smarter, real-time traffic management.

The AI system processes thousands of video frames per second to identify firearms in real-time.

By integrating AI-powered vision technology with the Lanner EAI-I731 Edge AI Appliance, hospitals can automate drug inventory tracking, ensuring accurate real-time stock monitoring

Smoke detection is a cornerstone of fire safety, as smoke is often the earliest indication of a potential fire hazard. However, traditional smoke detectors can be delayed in identifying smoke, particularly in expansive spaces or areas with high ceilings. Addressing these limitations, the Lanner Edge AI Security Solution—AI Smoke Police (AISP)—offers a sophisticated, AI-based approach to real-time fire and smoke detection, ensuring quicker response times and enhanced safety measures.

As the global energy grid faces increasing challenges from aging infrastructure and insufficient automation, advanced monitoring solutions are essential to protect critical infrastructure such as substations, drilling rigs, and oil and gas facilities. Modern technologies, such as AI-powered computer vision and thermal detection systems, provide comprehensive solutions to mitigate risks, enhance safety, and ensure operational continuity.

The prevalence of Video Management Systems (VMS) infrastructure has now reached a point where enterprises find it impractical to continuously monitor the overwhelming stream of video data around the clock.

Real-time traffic video analytics, built on IoT and AI, allow for the collection and analysis of data to improve day-to-day traffic management, enabling a wide range of advanced applications, such as traffic flow analysis, smart traffic management, and violation detection, and assisting cities adapt for long-term sustainable transportation needs.

We provide system integrators and OEMs with advanced video analytics. Our AI solutions are simple to integrate, and run on edge, hybrid, or cloud.

Lanner’s LEC-2290 and LEC-2137 edge nodes have the performance and remote management features necessary to circumvent edge-to-cloud roundtrip bottlenecks and deliver speedy pre-processed insights from the edge for real-time AI Inference with rock-solid reliability.

A data-centric AI approach provides a systematic method for improving data, reaching a consensus on the data, and cleaning up inconsistent data. With such an approach, teams can work in parallel and directly influence the data used for the AI system. By removing unnecessary back and forth among groups and looping in human input at the point where it’s needed most, the result is reduced development time.

Community leaders and law enforcement agencies are dedicated to reducing crime and enhancing safety for residents across all settings, including local streets, public transportation, shopping centers, entertainment venues, school campuses, and medical facilities.

Retailers and manufacturers are using computer vision technology to uncover real-time insights into business operations.