How Computer Vision is Revolutionizing Logistics and Supply Chain Management?

How Computer Vision is Revolutionizing Logistics and Supply Chain Management?

Dorota Owczarek - July 8, 2022

AI-based computer vision is changing the way businesses operate and manage their supply chains. By using image and video analysis, computer vision can help identify problems and optimize processes in logistics, transportation, and supply chain management. Some of the benefits of computer vision in logistics include increased efficiency, improved safety, and reduced costs. In this article, we will discuss how computer vision is revolutionizing logistics and supply chain management. We will also look at some use cases where computer vision has been successfully applied.

What Is Computer Vision, and What Are Its Applications?

In order to understand the way computer vision works, it’s worth breaking down the mechanism it reflects. How do humans see? In a nutshell, our eyes capture the image later processed by the retina, the cells of which send impulses to the brain. There – specifically, in the visual cortex – this image gets interpreted.

In the case of machines, the whole process is very similar. We have a capturing device, which is a camera, and an interpreting one – a computer equipped with an artificial intelligence algorithm. Armed with AI, the machine can make sense of the images just like humans do. Companies from different industries take advantage of this capability, using computer vision to streamline their processes. It’s widely used in production for the purposes of controlling the assembly line, but also in logistics – for quality inspection, monitoring, sorting, optimization, etc.

What exactly can the computers do with the images with a little help from machine learning techniques? Let’s get to the basics.

Computer Vision and Machine Learning

Once the captured image gets to the interpreting device, it gets processed by the machine learning algorithm. In most cases, modern computer vision-based systems involve neural networks. Understanding image is a complex task, and deep learning can handle that complexity with its layered comprehension process that mimics the neural activity in the brain. Computer vision systems can carry out specific tasks, depending on the purpose they were built for. We’ve sorted them from the least to the most complex ones.

Object or Movement Detection

Detection is the most basic computer vision task that doesn’t require the computer to actually understand what it’s seeing but rather acknowledge that something is visible. Trained with the dataset containing pictures of objects or motion with annotated bounding boxes, the AI model can quickly learn to detect them even in detailed images.

Image Classification

In this case, the algorithm applies a class to an image based on its knowledge drawn from the database. For example – you want it to tell you whether it’s an apple or banana. Trained with a labeled database containing both apple and banana images, it learns to distinguish the two categories.

Deep Neural Network (DNN) for logistics image analysis

Deep Neural Network (DNN) - example of logistics image analysis

Image Segmentation

By identifying a segment of an image based on specific criteria, for instance, color or grey value (in the case of achromatic pictures), the AI is able to find the area of focus. It’s a great feature to use, for instance, in the insurance claims evaluation systems. The algorithm can quickly identify a space to evaluate within the provided image documentation of the accident based on the keywords in the claim or other indication.

Image Recognition

As a set of methods that allows the algorithm to recognize different variables (like objects, locations, logos, etc.), image recognition puts together the tasks of detecting and classifying elements in an image. The neural network fed with an appropriately prepared dataset learns to recognize the pixel patterns that imply a particular object. Image recognition is extensively applied in, for instance, authentication systems, usually with the support of Convolutional Neural Networks (CNN).

Convolution Neural Network - Feature extraction with convolutions

Convolution Neural Network - Feature extraction with convolutions

Image Generation

GANs (Generative Adversarial Networks) create new images that can later serve for various purposes – from data augmentation to personalized marketing. Image generation isn’t actually that complex – the tricky part is getting it right with the realistic visuals. Achieving a realistic look requires tons of visual data to feed the neural network with – otherwise, the generated image will look more like a patchwork.

In fact, the ML models cannot come up with an entirely new image. Just like our brains while dreaming, they combine the features they’re already familiar with. The more data they will draw from, the more smooth and natural that merge will be. It takes some effort (and computational power), but we’re getting better at this! Have you had a chance to see the viral Tom Cruise deepfakes? They’re eerily accurate, particularly when it comes to facial expressions.

Tom Cruise’s deepfake videos prove we’ve reached a new level when it comes to image generation.

AI-Based OCR

Optical character recognition is a branch of computer vision that enables a computer to interpret handwriting and transform it into digits, as well as digitalize paper documents via pattern recognition and feature extraction. It’s often paired with Natural Language processing for the purposes of electronic documentation systems.

Computer Vision Systems and the Emerging Tech of 5G and IoT

From the end user’s perspective, a 5G network equals, first of all, incomparable speed. Business owners gain much more with it, particularly if they employ artificial intelligence-based solutions. With 5G and IoT, companies can engage AI in internal and external processes to a much greater extent. It’s indeed a powerful trio!

First – because 5G enables companies that use IoT to go fully wireless without compromising the connectivity. Secondly – the extremely low latency of 5G makes the automated equipment – for instance, automated forklifts or vehicles – respond much faster, which has a critical importance in terms of safety.

That’s just a sneak peek of what this combination is capable of. You can dive deeper into this topic with our colleague’s insightful blog post.

Most Prominent Use Cases for Computer Vision in Logistics and Supply Chain Management

Together with advanced predictive analytics, computer vision opens a new door to logistics companies and businesses that are an essential part of the supply chain. Applying these tools, they can automate logistics processes in a way no one even imagined a decade or two ago, making some systems fully autonomous. That creates great opportunities in terms of error prevention as well as effective use of space. Not only the company itself can benefit from computer vision-based automation. The customers and environment benefit, too. In which ways? The use cases below illustrate that.

Computer Vision Systems for Inventory Management

With its demand fluctuations, the pandemic was a test for the classic inventory management methods. Unfortunately, they mostly failed it. But fortunately, there are new AI-fuelled solutions on the rise that address this issue.

Since the geopolitical situation is causing further instability in the supply chain and fueling rapid changes in demand (due to currency value drops, stockpiling, etc.), new approaches are more than necessary. That’s because these factors make it harder to track the stock and control it, which can lead to excessive waste and warehouse space shortages.

Computer vision systems streamline inventory management by increasing its visibility. They scan the captured images of stock, verifying the products’ state, the length of their shelf life, and the information on the labels. The cameras can track the inventory movement via barcodes, making it much less likely for the products to get lost somewhere in the process. Computer vision-powered inventory management also reduces the chances of assortment shortage.

Sorting and Collection Systems

With computer vision, the sorting process is relatively easy to automate, particularly in warehouses, where the items are usually captured within a similar setting. Because of that, it’s not that hard to train the algorithm to deliver accurate results. All the computer needs to do is perform a simple classification task. The scanned image of each product gets an attributed class; on this basis, the machine makes an automated decision to place it in a particular zone or send it elsewhere.

Related case study: Developing an X-ray imaging system for customs control with AI and machine learning algorithms

Ministry of Finance and their customs officers are manually analyzing x-X-ray images scanning containers and vehicles for illegal objects and tax evasion activities.

Our challenge? The goal of the project was to streamline the customs inspection process by automating prohibited objecs detection for X-ray images of containers and trucks. We delivered a state-of-the-art tool which uses AI and machine learning to make customs checks much faster while effectively improving detection rates.

This ambitious project required the development of a proper infrastructure for using the AI system in production mode. We created the API for integrations with third-party web services. Thanks to proper analysis and augmentation methods, the system is fed valuable data as it is used, and harnesses the data to train itself to achieve even higher efficiency and accuracy.

Read more about this case study.

Warehouses Visual Inspection – Defect and Anomaly Detection

In terms of inspection, computer vision is a game-changer for logistics companies. Instead of verifying each parcel manually, they can let the machines do the job and focus on more demanding, higher-value tasks. Trained with depictions of flawless products, the AI models quickly detect any defect or anomaly. Based on the output, the computer vision-powered system makes an autonomous decision to redirect the item for manual inspection. Such a solution offloads the employees while helping the company maintain the highest customer satisfaction rates.

Equipment Detection and Movement Analysis

Aside from the parcel inspection, the companies can also use computer vision to control the safety of the warehouse workers through automated detection of personal protective equipment. Since many workplaces do not have a specific role to perform such tasks, PPE verification often ends up being pushed aside. Equipment detection with computer vision allows the companies to control this aspect without human supervision.

Movement analysis is another exciting opportunity computer vision creates for the logistics industry. One thing this task can serve for is measuring the employees’ effectiveness. By analyzing the tracked movements of the workers and equipment, artificial intelligence can find bottlenecks in the warehouse processes. Another role of movement analysis is streamlining the logistic processes through gesture recognition. Instead of entering commands directly on the device, the employees can use their gestures or their combinations, saving quite some time.

How does it work in practice? We’ve previously covered the topic of gesture recognition to the smallest detail in this article.

Computer Vision for Infrastructure Security Capabilities

Whereas standard monitoring requires employees’ engagement, computer vision-based monitoring systems can be fully automatized. Not only does it cut expenses, but it also reduces the probability of human error. It is less likely that the AI-fuelled system will miss a thing if only the model is well-trained.

Lack of focus, short attention span – these issues don’t apply to machines. The companies can also employ drones to make their monitoring more complete and get access to places that were not accessible with traditional cameras. Plus, computer vision adds another dimension to authorization processes, constantly scanning the vehicles and equipment.

Organizing Containers Positioning

The current container crisis, the roots of which we have described in our maritime industry-related article, forces the shipping companies to adopt AI-based solutions. Computer vision can help them make the most out of the available space without extending their vessels in size. The AI-based systems are able to predict the most space-saving configuration of the containers and make the automated positioning equipment execute it with the help of computer vision.

AI-Based OCR Supporting Back Office Logistics Processes

Aside from streamlining logistics processes on display, computer vision can be helpful behind the scenes. It allows the logistics companies to smoothly scan crucial information and process it in their systems instead of entering the data manually. That, of course, speeds up the logistics processes and reduces the probability of typing mistakes that could make parcels untraceable.

Intelligent document processing with AI-based OCR solutions

Intelligent document processing with AI-based OCR solutions

The optical character recognition technology also streamlines returns. Even if the parts get back to the warehouse without any documentation, the employees can obtain information regarding them by scanning the serial number. And even more importantly, OCR is a foundation of the ID-recognition systems for parcels, containers, and vehicles. They allow the logistics companies to thoroughly control the delivery processes, track them and gather the metric. Thanks to these, the customers can seamlessly use the tracking systems to see where their order is and when it may arrive.

Drone-Based Machine Vision Technology

Drones bring the capabilities of computer vision monitoring systems to a new level as a replacement for stationary cameras. Contrary to them, they can capture the workplace and the equipment from various different angles, making anomaly detection much more efficient.

The mechanism stays the same. The interpreting device receives a real-time image captured by a drone; the image gets broken down by the neural network that detects particular objects, motions, or characters. On this basis, the image recognition systems make autonomous decisions or issues alerts and authorization requests to entitled employees.

Employing drones is a great way to streamline inventory management, particularly in big warehouses and distribution centers. The drones can scan the barcodes and tags, find parcels without human intervention, count inventory and detect anomalies, enabling the employees to complete the orders faster.

Autonomous Warehouses and Vehicles

Instead of wasting time searching for employees with very specific qualifications, logistics companies are starting to fill the staffing gaps with partial automation.

Autonomous forklifts solve the issue of shortages of forklift operators in the job market while reducing the costs of warehouse maintenance, and it’s possible due to computer vision. It allows them to navigate the warehouse space autonomously, detecting obstacles, finding the items and positioning them, or taking them for transport.

The autonomous forklifts are usually a part of the complex intralogistics system that involves automated stackers and pallet conveyors. Connected via the Internet of Things, the equipment is fully trackable, and its parameters – easy to monitor.

Logistic suppliers can take a step further and automate the movement outside of the warehouse with autonomous vehicles navigated with computer vision. So far, autonomous cars remain a novelty and are not widely used in logistics, but that may change in the nearest future.

Autonomous Vessels and Port Operations

Ports set the trends for the logistics industry as a whole, already extensively using autonomous vehicles to streamline the transport of containers within the port area. Many ports in Asia are already fully autonomous thanks to advanced predictive analytics combined with computer vision. They use automated computer-vision based systems to load and offload the vessels and manage the cargo transportation and identification.

Autonomous vessels aren’t a novelty, either. So far, the autonomous ships market has been growing steadily year after year and is expected to reach almost 10 billion USD in 2025 (Autonomous Ships Global Market Report 2021). At this point, the fully autonomous units make a tiny part of the automated vessels, but that will be gradually changing with an advance of predictive analytics and computer vision. The latter enables them to safely navigate and dock in ports.

Get Started With Computer Vision in Your Business

Computer vision has already changed the face of the logistics industry. First employed by the big players on the market like Amazon, now it’s becoming a popular way to gain a competitive edge. Aside from logistics, the technology also finds applications in the manufacturing and industrial sectors, often fulling similar functions – from monitoring and quality control to anomaly detection and warehouse management.

Whether you want to employ computer vision in these fields or have some other idea for using it for your business, we would love to help! Our company has been providing computer vision development services for quite some time already, creating complex systems like, for instance, an X-ray imaging system for custom control ( case study). We know how to kick off such a project and bring it to an end with success. Contact us to tell us more about your vision!

About the author

Dorota Owczarek

Dorota Owczarek

AI Product Lead & Design Thinking Facilitator

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With over ten years of professional experience in designing and developing software, Dorota is quick to recognize the best ways to serve users and stakeholders by shaping strategies and ensuring their execution by working closely with engineering and design teams.
She acts as a Product Leader, covering the ongoing AI agile development processes and operationalizing AI throughout the business.

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This article is a part of

AI in Logistics
25 articles

AI in Logistics

Artificial Intelligence is becoming an essential element of Logistics and Supply Chain Management, where it offers many benefits to companies willing to adopt emerging technologies. AI can change how companies operate by providing applications that streamline planning, procurement, manufacturing, warehousing, distribution, transportation, and sales.

Follow our article series to find out the applications of AI in logistics and how this tech benefits the whole supply chain operations.

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