CRON Systems develops senseEDGE, a deep learning platform that processes live 3D sensor data from LiDAR and other sensors to achieve high-accuracy object detection, classification, and tracking. This technology enhances traffic management and urban infrastructure by providing real-time situational awareness, even in challenging environmental conditions.
Funding
$4.1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
KVVYVFounders
Product
Problem
Existing traffic management and urban infrastructure systems struggle to achieve accurate object detection, classification, and tracking in real-time, especially in adverse weather conditions or partial visibility. Traditional methods often fall short in providing comprehensive situational awareness, hindering the efficiency and safety of urban environments.
Solution
senseEDGE is a deep-learning platform designed to process live 3D sensor data from LiDAR and other sensors, enabling high-accuracy object detection, classification, and tracking. This technology enhances traffic management and automated infrastructure by delivering real-time situational awareness, even in challenging environmental conditions. senseEDGE uses deep learning algorithms trained on diverse datasets to provide accurate and reliable perception data. The platform scales from single-sensor solutions to complex deployments, meeting the needs of various technology partners. By extracting rich intelligence from live 3D sensor data, senseEDGE empowers intelligent transportation, smart spaces, smart security, and automotive applications.
Target Audience
The primary customers are traffic management systems, smart city developers, and security providers seeking to enhance situational awareness and improve the efficiency and safety of urban environments.
Features
- Processes live 3D sensor data from LiDAR and other sensors
- Employs deep learning algorithms for object detection, classification, and tracking
- Provides accurate perception in challenging environments, including adverse weather and partial visibility
- Offers a plug-and-play deployment with minimal setup and configuration
- Achieves seamless object fusion, combining LiDAR data with information from other sensors (cameras, radar)
- Can be quickly trained for new use cases through simulation techniques and supervised learning
- Supports multiple sensors to increase coverage area and improve point quality
- Offers a configuration utility and visualizer accessible through a web browser