Visual Positioning System with RedX
Case Study: Visual Positioning System.
Background:
In the fast-paced telecommunications industry, ensuring the safety of workers during the construction and maintenance of tower infrastructure is paramount. A leading tower company, which specializes in erecting and leasing tower structures to telecom operators, aimed to elevate its safety protocols. To achieve this, they turned to innovative AI technology and partnered with us to deploy a state-of-the-art solution.
The Challenge:
The client wanted an AI-powered system to accurately identify the position of individual items within a warehouse that could reduce the time taken to locate items, and consequently streamline their inventory management strategies.
The Solution: RedX AI Automation Platform:
To calculate the exact geolocation (latitude & longitude) of items, we designed a platform that could map the location of objects on Google Maps, providing a simple and familiar user interface.
For the hardware setup, we deployed PTZ (pan, tilt and zoom) cameras as well as fixed cameras that could track the forklifts’ movement and monitor the position of each rack within the warehouse.
The PTZ cameras would continuously scan the forklifts’ movement in their respective visual zone. Once a camera detected a loaded forklift near the stacking area for at least 30 seconds, our computer vision solution would calculate the exact position of the forklift (latitude and longitude) using the camera’s location.
Once the forklift operation is completed, the PTZ camera can zoom into the rack and find the QR Code associated with that rack. Once the QR Code is detected, the camera can read the QR Code and store precise information about the rack and its position directly into the warehouse database.
Similarly, other cameras will scan QR Codes of other racks to store item-position information on the database. When a user wants to find a particular item, they can simply use the solution to enter the item details into the search bar. The system searches the database and fetches complete information about the particular item, including its exact position on the rack and geo-location with a spatial resolution of 5 centimeters.
Impact & Results:
The deployment of the RedX platform transformed safety monitoring from a manual, error-prone process to an automated, efficient, and reliable system. The ground-based human tracing cameras provided comprehensive coverage of the site, enabling real-time detection and alerts for any safety gear non-compliance.
This not only streamlined the monitoring process but also significantly enhanced the safety culture among workers. The tower company experienced a marked reduction in safety incidents and non-compliance, highlighting the effectiveness of integrating AI technology into safety protocols.
Conclusion:
The integration of RedX at the tower company’s construction sites has set a new industry standard for safety and efficiency. This case study exemplifies our dedication to harnessing AI for meaningful applications, ensuring safer work environments, and leading the charge in technological innovation within traditional sectors.
This case study highlights our commitment to improving workplace safety through technological innovation, demonstrating how AI can be leveraged to make traditional industries safer and more efficient.