Equipment Inspection

Automated safety and compliance monitoring to minimize accidents and increase efficiency.

What is Equipment Inspection?

Computer Vision is used to detect the presence of workers, equipment, and materials such as protective equipment. AI vision helps to improve safety and productivity in manufacturing and construction.

Deep learning algorithms can accurately detect objects in plants and on construction sites by processing real-time video streams of cameras such as surveillance or IP cameras.

Key Features of Equipment Detection

Real-time object detection for the inspection of equipment is non-invasive, scalable, and comparably easier to implement compared to physical sensors. A camera captures more information dimensions compared to BLE or RFID chips.

  • Detect and monitor PPE usage (Personal Protective Equipment) by helmet detection, eye protection detection, vest detection, and more.
  • Detect heavy construction equipment such as excavators, cranes, generators, or tractors.
  • Detect the presence of workers in dangerous areas, for example, in close proximity to excavators.
  • Detect persistent or reoccurring violations of safety protocols in plants or on construction sites.

Why is Equipment Inspection used?

Deep Learning based equipment detection and inspection is used to increase efficiency and safety, leading to cost savings.

  1. The automation of equipment inspection is more consistent and accurate than human inspection.
  2. Increased safety by lowering the risk of accidents, infrastructure damage, and catastrophic events.
  3. Save costs of manual inspections, lower insurance costs, and increase efficiency and productivity.
Last updated
September 18, 2021
Requires Viso Suite

How to get started

This solution is built on Viso Suite, the all-in-one computer vision platform to deploy, run and scale the solution with powerful tools and infrastructure.

To get started, request a personal demo, we will put together a plan that includes all the services you need to get started.

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