Page 780 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

Lessons learned from production deployment:

1. Deployment was extremely challenging, especially when scaling up to connect to 100s
of cameras.
2. MLops requires experience in optimizing AI models through pruning and quantization.
This must be done carefully, as it might result in reducing accuracy.
3. Acquiring hardware is difficult due to GPUs procurement being slow due to US restrictions.

Impact on Intelligent Transport and Environmental Safety:
Tanbeeh leverages AI-driven computer vision to transform safety, security, and operational
efficiency within industrial environments. By automating fire detection, PPE compliance,
and traffic management, it strengthens infrastructure resilience and supports safer, smarter
operations at large-scale logistics hubs like DP World.

Its AI-powered traffic analytics optimizes logistics and resource allocation, significantly
improving industrial efficiency. By reducing congestion and enhancing traffic flow, Tanbeeh
contributes to smarter urban mobility systems. Real-time traffic monitoring minimizes delays
and enables better coordination between logistics centers and city infrastructure, facilitating
seamless transport operations.

Traffic congestion is a key source of carbon emissions. Tanbeeh’s AI-driven optimization
reduces vehicle idle times, lowering greenhouse gas emissions and improving air quality in
urban and industrial areas. Additionally, early detection and prevention of industrial fires reduce
air pollution and toxic emissions, supporting cleaner environments and public health.

Beyond industrial applications, Tanbeeh’s early smoke and fire detection capabilities can be
extended to forest conservation efforts. By identifying potential wildfires before they spread,

744