Computer Vision Logistics Use Cases: A Guide
Computer vision technology has advanced significantly over the past couple of years, making its way into applications across industries like supply chain and logistics.
From visual inspection systems to automated robotic systems, computer vision increases logistic productivity and helps workers by making their tasks easier.
As industrial companies work to keep up with cutting-edge technology, the demand for real-time data and automation is driving the adoption of computer vision.
In this article, we’ll explore how computer vision is used in logistics, and also take a look at case studies from leading companies like Amazon, DHL, and UPS. Let’s dive in!
Computer Vision in Logistics Use Cases
Let's talk through three areas in which computer vision is used in logistics:
- Warehouse automation and inventory management
- Automated sorting and packaging
- Quality control in shipping
- Robotic picking and packing
Warehouse Automation and Inventory Management
Many companies today are using AI to automate their inventory management systems. These systems use computer vision, along with cameras and sensors, to track and manage inventory in real time. Computer vision systems streamline the automatic identification, tracking, and counting of items, as well as updating inventory records and triggering reorders when stock is low.
How does this work? High-resolution cameras and sensors are placed throughout a warehouse to constantly capture images and video of the inventory and storage areas. The visual data is then processed by computer vision algorithms that can recognize items in the warehouse like packages, read their barcodes or QR codes (using OCR), and track the quantities and conditions of the stock. As items are added or removed, the system automatically updates the inventory records in real time. It’s integrated with the warehouse management system, ensuring that all inventory data stays synchronized and up to date.
Automated Sorting and Packaging
Another important computer vision use case in logistics is the automation of sorting and packaging operations. AI robots can used for both of these tasks.
Robots with sensors, cameras, and mechanical arms can accurately detect and classify items using computer vision, then change the route a package takes on an assembly line based on the type of package (i.e. whether a package is a box or a letter, or whether it is plastic-wrapped or cardboard).
Similarly, automated robotic packaging systems can pick up, move, label, seal, and pack products continuously without assistance. Some of these robots are also very compact, making them ideal for tight spaces in manufacturing facilities. An added benefit of computer vision-based robots is that they free up human workers to focus on more complex tasks.
Quality Control Operations in Shipping
Manual quality control operations can often result in human errors due to factors like miscalculations and worker fatigue. A much more reliable solution is to use computer vision. These systems use cameras to capture images of products on a production line. The images are then analyzed to detect defects or inconsistencies. Such systems have a wide range of uses within warehouses, including inspecting product dimensions, packaging, and surface quality.
In addition to detecting defects, computer vision AI can also help with extracting valuable information from product labels. By using techniques like optical character recognition (OCR) for reading text, barcodes, and QR codes, these systems can verify product codes, descriptions, and expiration dates. The extracted data can then be compared to the data in the warehouse management system (WMS) to ensure accuracy and prevent errors in inventory management.
Robotic Picking and Packing
Robots integrated with computer vision are becoming vital for picking and packing products (also known as palletizing). It involves arranging products onto a pallet for storage in a warehouse or for transportation, while depalletizing is removing products from pallets.
Similar to other vision-capable robots used in warehouses, they use cameras, sensors, and algorithms to capture images of products to determine their size, shape, orientation, and position. The collected data is then analyzed for choosing an optimal arrangement for packing the product. The system can also determine how to pack the products to maximize space and stability.
Role of Computer Vision in Supply Chain Optimization
Now that we’ve discussed some of the applications of computer vision in logistics, let’s explore how can be used to optimize other parts of the supply chain.
By analyzing video feeds from cameras placed at key points in warehouses, companies can collect a lot of data. The data can be used to track the movement of goods and supplies in real time, from manufacturing to final delivery.
Doing so helps ensure timely shipments, monitor inventory levels, oversee loading and unloading, and verify the condition of goods during transit. These systems can also monitor equipment like conveyor belts and forklifts for signs of wear and tear. Computer vision can enable the early detection of issues and the scheduling of preventive maintenance to avoid costly breakdowns. This level of visibility makes it possible for businesses to quickly address disruptions and maintain smooth operations.
Computer vision systems can also play a part in optimizing delivery routes and managing fleets. These systems can analyze traffic data, road conditions, and other factors to pick the most efficient routes for delivery vehicles.
The delivery vehicles can be tracked in real-time, both on roads and in parking spaces, helping with traffic flow monitoring and parking space utilization. Using data from cameras placed along roadsides, at traffic lights, and in parking areas, fleet managers can make dynamic adjustments to routes, avoid congestion, and maximize vehicle efficiency throughout the delivery process.
Benefits of Implementing Computer Vision in Logistics
Now that we've discussed how computer vision is used in logistics and helps optimize supply chains, let's look at the benefits it offers:
- Cost savings: Automating tasks with computer vision saves on labor costs and improves supply chain efficiency by streamlining operations and reducing reliance on manual processes.
- Improved safety: Computer vision can monitor warehouse floors and vehicles to prevent accidents and detect potential theft, increasing safety for employees and assets.
- Operational scalability: As businesses grow, computer vision systems can adapt to handle increased volumes without requiring proportional increases in staff, making scaling up operations more efficient.
- Improved quality assurance: High-resolution computer vision systems can detect product defects that may be missed by human inspection, maintaining higher quality standards and reducing returns or recalls.
Case Studies of Computer Vision in Logistics
The computer vision market is estimated to expand to over 175.72 billion dollars globally by 2032. Such a projection is due to its wide use by leading companies like Amazon, DHL, and UPS. Let’s understand how these companies are using vision-based technologies in their logistics operations.
Amazon’s Automated Warehouses
Amazon uses computer vision-based software and robotic systems for a wide variety of applications. For instance, they use computer vision to detect defects in products. Researchers at Amazon trained a machine learning model to compare images of products to how they should actually look. To do this, cameras scan every item that goes through the warehouse, and then the model analyzes the scans to detect defects.
Computer vision is also used in Amazon’s robotic systems, such as Sparrow, Robin, Cardinal, etc. Sparrow can identify, pick up, and handle individual products within the warehouse. On the other hand, Robin and Cardinal are used to handling packages after they're boxed. These innovations gave Amazon employees the ability to process over 13 million packages a day.
DHL’s Smart Glasses for Warehouse Operations
Computer vision and augmented reality (AR) are new innovations being used to modernize warehouse operations. Techniques like hands-free picking are made possible through AR smart glasses, and the technology has been met with positive feedback and high approval rates among DHL employees.
AR and computer vision techniques like object detection and recognition are integrated into a heads-up display that helps them locate, scan, sort, and move inventory without needing a handheld scanner or referencing hard-copy forms. DHL has gradually implemented AR picking processes, known as vision picking, across various geographical regions following successful test runs in the USA, Europe, and the UK.
UPS and Autonomous Drones
Flying drones delivering packages aren’t a thing of the future. They’re already here. UPS, one of the world’s largest logistics companies, is using drones equipped with computer vision to make deliveries. These drones can take off and land vertically in small spaces and then fly efficiently at high speeds. Thanks to computer vision, they can spot good and bad landing sites along the way. For example, drones can detect forest fires during deliveries and alert authorities in real-time. UPS has even used these drones to deliver COVID-19 vaccines during the pandemic to places like Atrium Health Wake Forest Baptist in North Carolina.
Conclusion
Computer vision is changing the logistics industry by automating tasks, making tasks more efficient, and reducing errors. From automating warehouses to tracking inventory in real-time, sorting items, and checking the quality of products, these technologies offer many benefits. Leading companies like Amazon, DHL, and UPS are already using computer vision to improve their operations in supply chain and logistics. As technology improves, we can expect to see even more efficient, affordable, and sustainable logistics in the near future.