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Trend Report: AI Driven Computer Vision

Leading the Dance Beyond Sight 

For good reason, AI is one of today’s most hyped topics and it comes in several different shapes and sizes. Arguably the biggest headline-grabbers right now are generative AI tools such as ChatGPT – technology that lets us have human-like conversations with a chatbot. However, another highly significant development in the AI space is AI-driven computer vision, a technology that is already deployed in a range of applications at an increasingly stable and dependable level.

This DHL trend report on AI-driven computer vision in logistics delves into the dynamic intersection of computer vision, artificial intelligence, and logistics, emerging as a compelling arena of transformation.  We think there has never been a more exciting time for industries and logisticians to work together to leverage the full potential of computer vision and AI for the benefit of organizations, our colleagues in operations, and for improvements in environmental sustainability.

But, as also outlined in the report, the integration of computer vision into logistics comes with challenges. As with any technological leap, there are considerations of data security, ethical implications, and the need for upskilling the workforce. The convergence of human expertise and AI augmentation requires thoughtful orchestration and collaboration – from all of us! - and we do hope that this report will contribute to this.

As we navigate the terrain of computer vision in logistics through this report, we invite you to explore the depths of this trend. Whether you're a logistics professional, a technology enthusiast, or an advocate for sustainable supply chains, this report offers insights into how computer vision is not only reshaping logistics but also propelling us toward a new era of interconnectedness and efficiency.

By working closely with our customers, jointly developing solutions and copiloting proof-of-concept projects in computer vision, we at DHL are staying ahead of the game. 

We believe in innovation beyond potential – there is always a better way to operate, plan, implement, connect, and share. As we seek to improve our own logistics capabilities and those of our customers, we constantly look for fresh approaches and valuable new technologies. 

That’s why we engage with the brightest tech innovators and disruptors around the world. If a technology development or application can contribute to a better customer experience, higher customer satisfaction levels, improved efficiency, and more sustainable operations, we’re interested! You’re welcome to explore many opportunity areas in our recently launched virtual ‘Warehouse of Innovation.’

We hope this trend report will inspire and guide you and we look forward to collaborating with you in this exciting and potentially transformative field of computer vision in logistics, powered by artificial intelligence. 

Dr. Klaus Dohrmann - Vice President, Head of Innovation & Trend Research

Computer Vision Looks Ahead

Companies are showing more and more interest in AI-powered computer vision technology, and an increasing number of technology providers are ready and able to supply this demand. The global computer vision market is building steadily, with researchers predicting growth from $9.40 billion in 2020 to $41.11 billion in 2030, a decade of CAGR (compound annual growth rate) at 16%.

As computer vision proves its worth in specific applications around the world, it already looks set to optimize many sectors. This trend report highlights the use of computer vision in many areas of logistics, from dimensioning and safety to route optimization and demand prediction. At the same time, it can also be applied in other industries such as – retail, healthcare, disaster response and recovery, and manufacturing. This illustrates the enormous potential this AI-based technology has in the supply chain.

The DHL Logistics Trend Radar identifies computer vision as a trend that will become part of the standard way of operating in the logistics industry within the next five years, underpinning and driving future logistics success by enabling more automated and efficient processes as well as sustainable and safe operations.

What is Computer Vision?

AI enables computers to “think” and computer vision allows computers to “see and understand.” Computer vision systems gather information from visual inputs like digital images and videos. By collecting and crunching this visual data using algorithms, these systems can then make suggestions and even perform actions.

Since birth, every sighted person has been learning how to tell objects apart, estimate object distance and speed, spot visual anomalies, and interpret what we see. This is the basis of AI-powered computer vision as well.

Computer vision systems, specifically their algorithms, must be trained in the same way, and this is done using visual data. The training process is accelerated by providing vast amounts of digital input. These systems never get tired and can quickly exceed our human capabilities of detecting and reacting to visual inputs. Computer vision accuracy rates for identifying and classifying objects increased from 50% to 99% in less than a decade.  

Current Impact on Logistics

AI is already impacting the logistics industry, from chatbots to route optimization and demand prediction. And now, computer vision looks likely to unlock many more opportunities, thanks to technology advances in depth perception, 3D reconstruction, and interpretation of dark and blurred images. 

How Computer Vision Systems Learn

Computer vision systems learn by looking at vast quantities of high-quality visual data. They repeatedly analyze this data until they recognize images and learn about image differences. How is that done? Using two different technologies:

  •  Deep learning, a machine learning approach, employs algorithms to autonomously extract insights from visual data through the utilization of artificial neural networks, continuously enhancing its knowledge from the available information.
  • A convolutional neural network (CNN), which breaks images down into tagged labels and performs the math on these labels to repeatedly check prediction accuracy.

Computer vision is likely to soon unlock many more opportunities, thanks to technology advances in depth perception, 3D reconstruction, and interpretation of dark and blurred images. It’s clear that deep learning has moved from the conceptual realm to practical application as many computer vision applications, from facial recognition to self-driving vehicles, make use of it.

Trends Linked to Computer Vision

A wide range of technology trends are linked to computer vision. Here are some key examples from the DHL Logistics Trend Radar.

Interactive AI

This refers to using AI algorithms that process human user input, like text and speech, to provide a reasonable response.

Edge Computing

Featuring decentralized IT architecture, this trend enables high-quality visual data from cameras and sensors at the edge of a network to be processed at high speed while keeping the information safe at the source.

Digital Twins

When integrated into digital twins, computer vision allows for remote monitoring of physical objects. It can autonomously identify flaws or deviations and promptly perform suitable actions to correct them.

Mixed Reality

Computer vision extracts data from images and videos. Mixed reality integrates it into the physical world by creating 3D overlays, providing guidance for many tasks like advanced inspections or complex surgeries.


By implementing deep neural networks, cameras mounted on drones can be trained to detect people and objects. Subsequently, they can analyze the images and communicate their findings in real time.

Big Data Analytics

This trend involves analyzing large data sets to find patterns, track real-time changes and forecast the future. In computer vision, it accelerates processes, enhances productivity, etc.

Outdoor Autonomous Vehicles

Computer vision is central to this technology as cameras and sensors combined with object detection algorithms help these vehicles avoid collisions, follow designated routes, and detect obstructions. 


Vision-based simultaneous mapping and localization enable robots to perceive, understand, and react to changes in their surroundings. Applications include plotting routes, mapping unmapped areas and avoiding obstacles.

How Computer Vision Creates Value

Today’s computer vision systems are deployed in various ways.

The most well-known application is image classification. The system sees an image and predicts it belongs to a certain class (e.g., a human, a pair of safety goggles, a forklift).

Another familiar application is object detection, also known as machine vision. The system not only classifies an image but also takes note of (tabulates) its appearance. Once an object has been detected, it can be tracked. Object tracking is often done using sequential images and video feeds.

A further use of computer vision systems is content-based image retrieval, which helps to increase the accuracy when it comes to searching for and retrieving digital images.

Computer vision imagery data is subjected to various processes including image processing (stitching, filtering, pixel counting), image segmentation (partitioning into multiple segments to simplify or change the representation into something that is meaningful and easier to analyze), blob checking (looking for discrete spots of connected pixels as image landmarks; blobs often represent optical targets for observation, robotic capture, or manufacturing checks), and pattern recognition (algorithm-based template matching to find patterns using machine-learning methods).

Image Segmentation

Partitioning into multiple segments to simplify or change the representation into something that is meaningful and easier to analyze.

Pattern Recognition

Algorithm-based template matching to find patterns using machine-learning methods.

Blob Checking

Looking for discrete spots of connected pixels as image landmarks; blobs often represent optical targets for observation, robotic capture, or manufacturing checks.

Image Processing

Stitching, Filtering and Pixel Counting.

Challenges in Computer Vision Applications

Focus. The computer vision model must receive highly specific training on a clearly defined problem to solve. 

Data Quality. Training models need vast amounts of visual data, and this must be of high quality. 

Model Selection. Each computer vision system must use the right model and modelling techniques. Off-the-shelf is not feasible.

User Adoption. To deliver value, the solution must be accepted by all users. 

Cybersecurity. Malicious data manipulation can skew analyses and alter AI performance. 

Balance. With so much visual data to store, process, analyze, and maintain, companies must balance cost and accuracy. 

Best Practice. Machine-learning operations (MLOps) and DataOps best practice is essential, especially using controlling data (versioning).

Privacy. To protect employees and boost acceptance, it’s important to incorporate privacy measures and comply with GDPR and other laws from the outset. 

Investment. Costs can include cameras upgrades, new tech investment, ongoing platform maintenance, and workforce upskilling and reskilling.

In the next chapter...

Computer vision is already proving its worth in a vast range of applications. In the next chapter we explore key solutions that are deployed in retail, healthcare, disaster response and recovery, and manufacturing. With these real-world computer vision examples, it is easier for us to understand the potential of computer vision applications in logistics and the supply chain.     

  • What exactly is computer vision?

    Computer vision allows computers to “see and understand” by gathering information from visual inputs like images and videos. It uses algorithms to analyze this data, make recommendations, and take actions.

    How do computer vision systems learn?

    They learn by analyzing vast quantities of high-quality visual data using deep learning algorithms and convolutional neural networks (CNN).

    What are some trends associated with computer vision?

    Key trends include Edge Computing, Digital Twins, Mixed Reality, Drones, Big Data Analytics, Outdoor Autonomous Vehicles, and Robotics.

    What is the current status of computer vision technology?

    The popularity of computer vision and its various areas of application are increasing. The global market is predicted to grow from $9.40 billion in 2020 to $41.11 billion in 2030.

    How is computer vision impacting the logistics industry?

    Computer vision is revolutionizing logistics in areas like dimensioning, safety, route optimization, and demand prediction. The DHL Logistics Trend Radar predicts it will become a standard in the logistics industry within five years.

    How does computer vision create value in applications?

    Applications range from image classification and object detection to content-based image retrieval. Processes include image processing, image segmentation, blob checking, and pattern recognition.

    What challenges are faced when applying computer vision?

    Challenges include focus, data quality, model selection, user adoption, balancing costs, best practices, privacy concerns, cybersecurity threats, and investment costs.

    How is computer vision proving its worth in various sectors?

    Computer vision technology is already showing its potential in retail, healthcare, disaster response and recovery, and manufacturing. It’s expected to have a significant impact on logistics and the supply chain in the future.

The Team

Dr. Klaus Dohrmann

Project Director & Co-Author

Emily Pitcher

Editor-in-Chief & Co-Author

Maulik Kamdar

Research Lead & Co-Author

Other Contributors
Amy Henshall
Angela Hills
Anna Finkbeiner
Bastiaan Snaterse
Ben Gesing
Christian Lundbak
Dina Falk
Graham Avery
Holger Schneebeck
Julian Selders
Kristin Szekat
Lars Pappe
Maida Ajmal
Noah Tombs
Olande Stols
Paul Schlinkert
Philip Jensen
Santiago Romero
Stefan Fuehner
Susanne Lauer
Torben Pagh
Zineb Darkouch

Marketing and Design Agency

Editorial Support
Words Europe