Vladimir Lebedev
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Product development & Implementation
Product development & Implementation
Functional Module: Computer Vision for Loaders and Excavators work control
Functional Module: Computer Vision for Loaders and Excavators work control
Budget: $100 000
Budget: $100 000
Timeline: 6 months
Timeline: 6 months
Scope & Objectives
Scope & Objectives
Scope & Objectives
In traditional fleet management system implementations, additional sensors were used to track operational cycles and technological processes. These sensors were typically installed on key machine components, such as the boom, arm, and bucket.
However, this approach lacks reliability, requires frequent maintenance, and is prone to failures, which negatively impacts the efficiency of the fleet management system.
I developed a computer vision-based solution that replaces these sensors with a camera installed in the operator’s cabin. This allows for automated tracking of work operations using machine vision algorithms. The solution is highly reliable and requires minimal maintenance, significantly improving the efficiency and sustainability of the fleet management system.
In traditional fleet management system implementations, additional sensors were used to track operational cycles and technological processes. These sensors were typically installed on key machine components, such as the boom, arm, and bucket.
However, this approach lacks reliability, requires frequent maintenance, and is prone to failures, which negatively impacts the efficiency of the fleet management system.
I developed a computer vision-based solution that replaces these sensors with a camera installed in the operator’s cabin. This allows for automated tracking
of work operations using machine vision algorithms. The solution is highly reliable and requires minimal maintenance, significantly improving the efficiency and sustainability of the fleet management system.
In traditional fleet management system implementations, additional sensors were used to track operational cycles and technological processes. These sensors were typically installed on key machine components, such as the boom, arm, and bucket.
However, this approach lacks reliability, requires frequent maintenance, and is prone to failures, which negatively impacts the efficiency of the fleet management system.
I developed a computer vision-based solution that replaces these sensors with a camera installed in the operator’s cabin. This allows for automated tracking of work operations using machine vision algorithms. The solution is highly reliable and requires minimal maintenance, significantly improving the efficiency and sustainability of the fleet management system.
I also have a publication on this topic, which I describe here
I also have a publication on this topic, which I describe here