Truck Tilt Detection with
RedX
Case Study: Truck Tilt Detection.
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:
Port and logistics businesses often face issues while loading/unloading containers from trucks. One common issue is that, on occasion, the RTG crane operator tries to lift the container off the truck before the locks of the container are opened.
Due to the non-opening of the container locks, the truck gets tilted and accidents may potentially happen, causing damage to the truck. In the other scenario, when RTG crane operators are loading containers on trucks, aligning the container with the truck bed takes time, and any misalignment can damage the truck or the container.
Hence, the client wanted a solution to detect if the truck is being lifted from the ground during unloading using computer vision and alert the crane operator to stop the operation immediately. Similarly, the client wanted a solution to help automate the alignment of the container and truck bed during loading of containers.
The Solution: RedX AI Automation Platform:
Unloading Operation Solution: We deployed LIDAR (Light Detection and Ranging) sensors on the sides of the RTG crane to align with the truck’s position. Using computer vision integrated cameras, the solution can accurately measure the tilt of the truck in case the crane lifts the container before the mechanical lock between the truck chassis and container is opened. Moreover, as soon as the truck tilts beyond a threshold, our solution alerts the crane operator and simultaneously activates a hooter in the crane operator’s cabin. To automate the stopping of the operation, the solution even provides a “STOP” signal to the PLC of the crane.
Loading Operation Solution: We developed a solution by setting up a specialized camera on the crane that aligned exactly with the top of the truck. The computer vision solution then calculates a virtual region, within which the truck must be aligned. The virtual region is visible to truck driver on a screen inside his cabin. Unless the truck driver aligns the truck bed to the virtual region, a red outline will be displayed on the screen along with an instruction to align the truck. During this time, crane operator is also instructed to stop (or slow down) to movement of the container towards the truck.
Once the truck is aligned within the virtual region, the virtual box has a green outline and the crane operator gets an alert on his screen to resume the movement of the container towards the truck. If the operator still continues to move the container towards the truck before it is aligned, our solution sends a command to the crane’s PLC system and stops the operation, while alerting the operator with a hooter.
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.