How Deep Learning enhances workplace safety – Overview

• An instance of a safety feature that helps drivers avoid collisions and reduces the probability of accidents is a collision avoidance system.
• Collision avoidance system will make use of a large variety of tools and sensors, such radar, lasers, cameras, GPS, and artificial intelligence.
• It is possible to minimise collision frequency and costs by 40%–60% on average by using AI sensors in the car to track driver movement, gaze direction/attention, automobile activity, traffic conditions, and other contextual data.
• The majority of collision avoidance approaches are based on a reliable tracking system that can detect the locations of people and objects to prevent them from hitting.
• To enable a human operator to perceive the relative positions of everything, tags can be mounted to tools and vehicles and carried by workers.
• These active safety systems incorporate alarms and alerts that ring when two tags are moved too closer together like part of their initial collision monitoring system.
• The method of changeable proximity detecting characteristics enables the creation of larger or smaller safe zones for people and equipment of varying sizes, permitting warnings to be customized to the scope and purpose from certain operations.

Why do Industries need collision avoidance systems?

• The primary objective of shipments and safety leaders is to keep drivers safe, successful, and feeling appreciated. They are aware that their drivers are their most valuable resource.
• However, they also recognise how important it is to maintain vehicle and driver productivity, lessen collisions, and cut maintenance and liability expenses. Many people struggle to strike this balance and rely on in-vehicle collision avoidance technologies to assist cut costs, prevent accidents, and protect their drivers.

How our deep learning solution helped One of Australia’s Vehicular interaction solution providers to enhance workplace safety.

• One of Australia’s Vehicular interaction solution providers approached OptiSol for a PAS (Pedestrian Avoidance System) to protect the staff and operators of the forklifts from harm.
• OptiSol designed a solution that uses Deep Learning to identify humans and calculates their risk in the proximity of forklifts.
• We used AI/ML to minimize false alarms by evaluating multiple danger factors around the vehicles through various AI/ML algorithms.
• Our designers also added enterprise features and management capabilities to the solution, notifying managers of incidents that would have gone unreported.
• Implementing this solution in one of their warehouses reduced the chances of human encounters with forklifts, reduced risks, and improved the safety of the workplace.

Architecture Diagram

Technology

Market Size: collision avoidance system

The global collision avoidance system market size was valued at $47.8 billion in 2020, and is projected to reach $114.5 billion by 2030, growing at a CAGR of 9.1% from 2021 to 2030.