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TREND REPORT: AI DRIVEN COMPUTER VISION

Leading the Dance Beyond Sight 

You are here

> With advancements in artificial intelligence (AI), computer vision is at a
> stage to become an industry-shaping technology and has exceptional promise
> along the supply chain – for our customers, employees, partners, and certainly
> the environment.
> 
> We at DHL are maintaining our commitment to shaping the Era of Logistics by
> bringing real-world innovations to the logistics ecosystem, leveraging AI and
> computer vision technology.

Katja Busch - Chief Commercial Officer DHL & Head of DHL Customer Solutions and
Innovation

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

AI-powered computer vision technology appears to have moved through the Gartner
Hype Cycle for AI to now enter the Plateau of Productivity. Companies are
showing more and more interest in computer vision, 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 USD $9.40 billion in 2020 to $41.11 billion in 2030, a decade of CAGR at
16%. 

As computer vision proves its worth in specific applications around the world,
it already looks set to enable 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 presents
application examples from other industries – retail, healthcare, disaster
response and recovery, and manufacturing – to illustrate the enormous potential
of this technology 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 successes 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 recommendations and even take 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 any image differences. How is that done? Using two different
technologies:

 * A type of machine learning called deep learning – this uses algorithms for
   self-teaching about visual data along with artificial neural networks to find
   out more and more from the data
 * 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 allows the processing of
high-quality visual data from the cameras and sensors - at the edge of a network
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 initiate corrective actions.




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.




DRONES

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 the findings in real time.




BIG DATA ANALYTICS

This trend involves analyzing large data 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.




ROBOTICS

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 protective
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 application for computer vision systems is content-based image
retrieval, to increase the accuracy of search and retrieval of digital images.

Computer vision images are subjected to various processes including:


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 APPLYING COMPUTER VISION

Focus. The computer vision model must get 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 controlling data use (versioning).

Privacy. To protect employees and boost acceptance, incorporate privacy measures
and comply with GDPR and other laws at outset. 

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




IN THE NEXT CHAPTER...

Already today, computer vision is 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 examples, we are better able to envisage the potential for
computer vision applications in logistics and the supply chain.

Continue to Next Chapter Continue to Next Chapter

NON-LOGISTICS USE CASE: RETAIL

NON-LOGISTICS USE CASE: HEALTHCARE

NON-LOGISTICS USE CASE: MANUFACTURING

NON-LOGISTICS USE CASE: DISASTER RESPONSE AND RECOVERY

LOGISTICS USE CASE: PEOPLE - HEALTH AND SAFETY

LOGISTICS USE CASE: PEOPLE - OPERATIONS

LOGISTICS USE CASE: ASSETS

LOGISTICS USE CASE: SHIPMENTS

OUTLOOK FOR THE FUTURE

 * 
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--------------------------------------------------------------------------------

--------------------------------------------------------------------------------


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
Archetype       

Editorial Support
Words Europe                                                     


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