AI-driven Computer Vision: A new lens into logistics

Imagine a world where machines see, learn, and work alongside humans. A place where technology illuminates things that are hard for people to recognize, shedding light on opportunities to improve efficiency and safety. For years it has been the stuff of science fiction, but now it is very much a reality.

In the swiftly evolving world of logistics, computer vision powered by artificial intelligence (AI) is emerging as a beacon of innovation. It’s redefining supply chain operations and helping light the way to greener logistics.

Let’s take a closer look at this transformational technology.

Computer vision involves developing technology that enables computers to interpret information from images and videos. It encompasses all kinds of image processing techniques and can be applied in many areas, from interactive art exhibits to medical imaging analysis.

We have followed this trend for some time, identifying it as a key driver for future logistics success. It will enable more automated, efficient, safer, and sustainable operations within the next few years.

The budding global market for computer vision is predicted to grow from USD $9.4 billion in 2020 to $51 billion by 2030. There’s a good reason for this. Computer vision has already proven its worth in many industries, such as retail and healthcare. And the use cases for logistics are steadily growing.

Our Customer Solutions and Innovation team recently took a deep dive into the dynamic intersection of computer vision, artificial intelligence, and logistics. A compelling story of innovation and transformation emerged – with many opportunities and challenges. Get all the insights in our Trend Report: AI-Driven Computer Vision.

It’s an exciting time for logistics companies like ours and our customers. Working closely with industries and tech companies, we can tap the full potential of the technology. There are so many opportunities to improve our operations, assist our workforce, and reduce our carbon footprint.

Below we highlight aspects of computer vision. Download the full trend report if you’re looking for the bigger picture. 

As mentioned above, computer vision allows computers to interpret and understand visual information. It mimics the human ability to recognize and process images and videos. At its core, computer vision involves identifying and classifying objects, people, places, and actions in digital images. This technology is quickly becoming crucial in various industries, offering transformative capabilities in fields ranging from logistics and retail to healthcare and security. By utilizing complex algorithms and vast datasets, these systems can analyze visual information at a scale and speed far beyond human capabilities. This opens doors to unprecedented efficiency and accuracy in image-based analysis.

AI-driven computer vision relies on a machine learning process called deep learning. At the heart of these systems are neural networks, which are algorithms modeled loosely after the human brain. To train these networks, they are fed large amounts of labeled data, such as thousands of images tagged with names or categories. The network then analyzes these training images and learns to distinguish patterns and features that differentiate one object from another. As the system is exposed to more data over time and adjusts its internal parameters, it becomes increasingly accurate in recognizing and categorizing new, unseen images. This learning process allows the system to make predictions or decisions based on visual data inputs, similar to how humans do it.

Think of it this way: Every sighted person learns to interpret what they see since birth. For a computer vision system to learn, it must first be shown lots of things and told what they are.

Once trained, the system compares what it sees against the knowledge it has gained, identifying and classifying the components of the image accordingly. In logistics, this could involve recognizing and categorizing packages based on their size, shape, or labeling. The process involves several stages, including image preprocessing to enhance image quality, feature extraction to identify unique attributes, and classification to assign categories to the identified features.

Computer vision technology is continually evolving and improving as systems receive more data. Recent advancements in AI have made significant improvements in accuracy and increased speed. In less than a decade, accuracy rates for identifying and classifying objects have soared from 50% to 99%. 

Here’s a look at some primary image recognition applications for today’s advanced computer vision technology.

• Image classification
  This involves categorizing images into predefined classes. For instance, image classification helps e-commerce businesses sort products into categories like clothing, electronics, or home goods, enhancing search functionality and user experience.
  
• Image retrieval
  This application is crucial in digital libraries and archives for searching vast databases to find images matching a query. The technology can also be used to retrieve relevant images based on content similarity, which is invaluable in fields like historical research or digital marketing.
  
• Image segmentation
  Image segmenting divides a digital image into multiple segments or pixels to simplify its analysis. This is particularly useful in medical imaging, where it helps isolate specific structures in complex images like MRIs or CT scans, aiding in more accurate diagnoses.
  
• Pattern recognition
  This involves recognizing patterns and regularities in data. In security and surveillance, pattern recognition is used to identify suspicious activities or behaviors. It’s also used in facial recognition technology, with applications ranging from smartphone security to law enforcement.
  
• Blob checking for image landmarks
  This application is about identifying and analyzing specific points or ‘blobs’ in an image. In manufacturing, for example, blob checking is used for quality control, ensuring that products meet certain visual criteria. It’s also used in robotics for object detection and navigation.
  
• Image processing
  This broad application includes enhancing image quality, converting images into different formats, extracting quantitative data from images, stitching, filtering, and pixel counting. In social media, for example, image processing is used to apply filters or adjust image parameters like brightness or contrast, enhancing user engagement.
  

As you can see, AI-driven computer vision is powerful and versatile, with the potential to revolutionize a wide range of industries by providing more profound and actionable insights from visual data.

State-of-the-art computer vision technology can add value in a variety of ways. Here are several significant advantages:

• Speed and efficiency: AI algorithms can process and analyze images much faster than humans, making them highly efficient for tasks requiring rapid analysis.
  
• Accuracy and consistency: Once properly trained, AI systems can become highly accurate and consistent.
• Scalability: AI systems can handle large volumes of data. They can process thousands of images simultaneously.
• Cost-effectiveness: Automating image analysis with AI can significantly reduce labor costs and time.
• Advanced capabilities: AI-driven systems can detect subtleties in images that may be invisible to the human eye, such as slight color variations or patterns.
• 24/7 operation: Unlike humans, AI systems can operate continuously without fatigue.

AI-driven computer vision can enhance the capabilities of a wide variety of technologies. Here are some examples:

Incorporating computer vision into logistics operations will play a crucial role in driving the industry toward more sustainable and environmentally friendly practices. Many of the advantages listed above will improve energy efficiency and reduce emissions.

For example, we can use these systems to streamline warehouse sorting and handling processes, reducing energy consumption and waste. They can analyze dimensions and optimize packing and storage, leading to more efficient use of space, fewer trips, and reduced fuel consumption, thereby lowering emissions. Computer vision can also enable asset monitoring and predictive maintenance systems that automatically detect defects and schedule repairs and upkeep. This extends the lifespan and reduces waste.

What’s more, by giving us that lens into so many parts of the supply chain, computer vision can help us identify and improve inefficiencies.

The logistics industry can benefit greatly from computer vision. The technology has advanced enough to be utilized in many ways across supply chains and logistics operations – it can support and improve many tasks in warehouses, distribution centers, and hubs.

As we embrace this technology, it will play an integral role in the digital transformation of logistics and supply chain management. And help pave the way for more intelligent, resilient, and sustainable supply chains.