We will be reviewing a machine vision system for light rail collision avoidance. This solution allows a light rail to avoid unexpected obstacles down the rail and reduce the chances of a collision. It uses an onboard industrial-grade edge AI appliance EAI-I130 provided by Lanner, capable of running MV models onboard. This appliance brings machine vision capabilities as close as possible to the light rail, allowing data captured by sensors to be processed right on-site.

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. A good urban traffic management network will not only improve traffic safety and boost local economies, but it will also improve public health and protect the environment.

Communities across the globe are grappling with the consequences of climate change, ranging from devastating floods to wildfires. As a result of abnormal climate patterns, forests are becoming increasingly parched, and heat waves have begun to emerge, exacerbating the destructive nature of wildfires. To effectively address this challenge, we explore innovative solutions to help communities prepare for and respond to climate-related disasters and threats.

Retail (brick-and-mortar and e-commerce) can leverage computer vision to analyze image data and generate insights. With video cameras around a retail store collecting digital images and a Deep Learning (DP) model, the retail store could identify and classify objects accurately and react immediately to what they see. But implementing Computer Vision can be pretty challenging because it needs large sets of data and a scalable and manageable computing power that only the cloud could give.  

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