25+ Applications of Computer Vision in Logistics

The use of computer vision in logistics is becoming more and more widespread. Hence, AI vision is an emerging key technology in the field of logistics innovation and intelligent supply chains. Some innovations within categories such as AI visual inspection, analysis, and automated handling systems are leading the way within new technological trends in logistics.

In logistics and supply chain digitization with computer vision, image processing influences the handling and flow of goods, the automation of logistical processes and supports human operators in logistics systems with cognitive, physical, and visual assistance purposes. In the following, we will outline the significant new trends in computer vision and image processing in logistics.

The implementation of smart logistics objects and smart logistics infrastructure aims to run processes as efficiently as possible. Therefore, sensor modules are used to gather information about logistics objects or infrastructure components. Computer vision allows efficiently digitizing objects, environments, and humans with visual sensors.

In recent years, machine learning and deep learning methods brought great breakthroughs in computer vision. Machine learning allows highly robust and accurate real-time image recognition and object detection. Compared to traditional machine vision, deep learning allows to perform video analysis using the video of common, inexpensive surveillance cameras and is highly scalable. If you are looking to build and operate your AI vision systems, check out the end-to-end computer vision platform VIMS.

How computer vision systems with deep learning work in a nutshell:

(1) Cameras provide the image or video

(2) Image processing algorithms analyze the visuals (AI model)

(3) AI model outputs are used to automate specific tasks

Scaling AI vision with IoT (Edge AI)

New trends move machine learning tasks from the cloud to the edge, closer to the sensor. Hence, AI vision is increasingly designed as Internet of Things (IoT) nodes. Such nodes are connected edge computing devices that perform image processing but do not deliver pictures but state data of processes.

Moving machine learning from the cloud to edge devices makes it possible to run deep learning everywhere and create scalable computer vision applications. Also, edge computer vision systems have the advantage of being robust, private, decentral, cost-efficient, and scalable. The ability to implement large-scale AI vision solutions enables a wide range of applications in logistics and distributed global supply chains.

Best Applications of Computer Vision in Logistics

The following is a list of the most important computer vision applications in Logistics. With the recent technological advances, we, at TEMPO Process Automation are continuously trying to adopt many more large-scale AI vision applications in logistics.

  • Traceability and tracking of objects
    • Vision-based goods identification
    • Container number recognition system
    • Good Localization System

  • Volumetric Properties of Goods
    • Goods and pallet dimensioning

  • Inspection and quality control of goods
    • Visual documentation and monitoring
    • Visual anomaly and defect detection

  • Equipment condition monitoring
    • Equipment condition inspection and monitoring
    • Early defect detection

  • Occupancy of storage and traffic areas
    • Determine free capacity of transport vehicles
    • Docks and parking lot occupancy detection
    • Storage bin occupancy analysis

  • Security and protection of infrastructure
    • Intrusion detection in logistics facilities
    • Crowd detection and behavior analysis
    • Vision-based Anomaly detection

  • Process modelling and simulation
    • Data collection for process analysis and optimization

  • Optimize manual picking and packing
    • Wrong placing detection with cameras
    • Human error detection with deep learning

  • Manually operated handling systems or vehicles
  • Automated handling systems

  • Visual documentation and Risk management
    • Real-time operations monitoring
    • AI vision-based risk management systems

Get started

If you are looking for ways to rapidly build and deliver computer vision applications in logistics, check out the computer vision platform, VIMS. The VIMS platform provides optimized all-in-one tools, AI algorithms, device management, and managed infrastructure to build, deploy and operate large-scale deep learning vision solutions.

Use visual no-code development, pre-built modules, and application templates to get results quickly – without developing everything from scratch. Learn about the features or reach out and contact our team to get a live demo.

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Matt Parks

About the Author: President & CEO, Matt has over 20 years building and leading high functioning teams
delivering exceptional results