Anti-collision monitoring systems in modern workplaces

Workplace collisions represent a major risk that can be unpredictable, resulting in serious injuries, costly damages, and operational disruptions. Industries around the world have faced these challenges since inception, often relying on traditional security measures that fail to ensure comprehensive protection.

According to IPAF Global Safety ReportCollisions with vehicles or machinery at workplaces remained among the top 5 causes of accidents from 2021 to 2023. A significant change can be seen in the construction sector where the number of deaths increased by 125% from 2022 to 2023.

Although it looks at one sector, there are cases around other industries such as manufacturing, logistics, oil and gas, and mining. Let's dive into the most common forms of conflict that rule around workplaces in these industries.

Workplace conflict can take many forms, each presenting unique risks and requiring specific precautions. Understanding these risks is critical to effective security management.

Human-machine collisions are a major concern in industries where workers work near heavy machinery. In a sector like construction and manufacturing, this scenario is very common.

Some of the frequent occurrences include-

• Forklift and pedestrian collision

• Conveyor belt and inspector collision

• Clash of dump truck and laborer

• Collision between overhead crane and maintenance worker

The general form of surveillance applied in such a situation relies solely on the observer's ability to monitor and judge the situation. This response may be strongly influenced by the supervisor's perception of proximity between machines and workers. Being human, this judgment may also vary from time to time based on their level of fatigue.

Machine-machine collisions occur when two or more pieces of equipment come into contact, often due to operator error, poor visibility, or system malfunction. In any workplace, from construction and manufacturing to oil and gas and mining, the involvement of machines is critical but can lead to serious collisions resulting in equipment damage, operational downtime, and financial losses. Losses have to be faced.

Here are some real-life workplace examples that lead to machine-machine collisions.

• Collision between tower crane and mobile crane

• Truck and loader collision

• Collision between drill rig and crane

• Collision of pallet truck and conveyor system

The impact of the collision of machines depends on the speed involved and the deceleration values ​​that must be kept in the loop during monitoring. Since these are mobile plants in operation, human monitoring systems may take longer to realize this and accident prevention procedures may be critical.

Collisions between machines and stationary objects, such as structures, barriers, or other equipment, can cause substantial damage and disrupt work flow. These collisions are often due to blind spots or lapses in manual supervision.

There are potential scenarios in all industries where accurate detection is required to avoid machine-object collisions.

• Collision between crane and overhead power line

• Collision between dump truck and temporary fence

• Truck and loading ramp collision

• Drill rig and pipeline collision

• Collision of maintenance vehicle and control panel

Distance calculations are important when placing machines and objects within the same range of activities. It needs to be fast and automatic to detect movements and revise the optimal distance between them.

This is where the use of AI-powered security monitoring can improve itself over manual monitoring processes. AI has a profound role to play in all three major forms of conflict across sectors.

Critical to collision detection is an automated and seamless monitoring method that can operate beyond human intervention. The integration of AI into collision detection systems overcomes this need and enables workplace safety, offering several key advantages over traditional methods:

AI-powered surveillance systems take advantage of advanced sensors, GPS and cameras to monitor the workplace with high accuracy. Sensors can cover a long range and algorithms help translate processed data in a fraction of seconds. This accuracy enables early detection of potential collision hazards, and allows timely intervention that prevents accidents.

The main feature of AI processed data is its real-time usage. It promotes the identification and analysis of threats as they arise. It includes –

1. Forward collision warning

2. Pedestrian detection

3. Unusual traffic jams

4. Blind spot warning

A comprehensive approach helps workers and machine operators detect potential collisions at the right time.

AI systems can automate safety protocols, such as slowing or stopping machinery when a potential collision is detected. The inclusion of assistance programs such as automatic emergency braking, use of the stability control system for adjustments, or parking assistance automates risk mitigation to a precise level.

AI-powered systems collect and analyze vast amounts of data from the worksite, providing insights into collision-related patterns and trends. Deep learning methods built into collision monitoring systems help retain data for longer periods of time and process every inch of detail simultaneously to predict future safety management tactics.

Comprehensive monitoring capabilities of AI systems enable better monitoring of worksite operations. By providing a clear and continuous view of the environment, these systems help ensure that security protocols are followed, and potential threats are addressed promptly. Effective operational monitoring also includes the ability to generate detailed reports and logs for compliance and review.

viActThe system performs detailed scenario-based analysis considering various operational contexts to identify potential collision risks. This can be achieved using 4 simple steps.

Step 1 – Install existing cameras on vehicles or machines.

Use on-site cameras with a resolution of 2 MP or higher and place them around vehicles or machinery including cranes, excavators, forklifts, or any elevated platforms.

Step 2 – Mark the danger areas.

Define or draw the areas to be marked as a. Danger zone When these machines work.

Step 3- Get automatic alerts on collision risks.

As soon as any worker, machine, or vehicle is identified as moving or standing within the danger zone, the computer vision technology incorporated in the system detects it and immediately alerts the relevant safety officers, supervisors and related workers. Sends an alert. Premises

Step 4- Use recorded data for safety management.

Records of every incident or near miss are saved in an intuitive dashboard. viHUB Which can be accessed at any time. It collects and analyzes data from multiple sources, providing detailed insights into safety trends and collision patterns.

These four steps are the basis for achieving a seamless AI-powered anti-collision detection system across all sectors.

viAct Uses advanced AI technologies including computer vision and generative AI for accurate and reliable collision detection. Here's a look at various AI-based tools to strengthen its collision avoidance system.

gave Danger Zone AI Alert System (DZAAS) Responsible for developing alert system in case of all modules. It uses both light and sound alarms that tick off within one second of detecting danger. It notifies via SMS/emails and records the instance in real time for future analysis.

viAct's innovative solutions exemplify the transformative potential of AI in creating safer, more productive workplaces. As the industry evolves, adopting these innovative safety systems will be critical in setting new standards for workplace safety and operational excellence.

Fascinated by viAct's anti-collision monitoring system?