Did you know workplace accidents in industrial sectors like logistics cost companies USD 62 billion annually? Most of these accidents are caused by human error, which is why technologies like AI, edge computing, and computer vision are creating a huge transformation. Computer vision in logistics is one such solution that is revolutionizing the industry with exponential ROI. From saving resources in packaging, preventing supplies damages by appropriate loading, and minimizing delays due to traffic conditions, custom computer vision system software development solutions are making the industry more lucrative by the year, improving its productivity, efficiency and scalability through minimal investments. No wonder the value of the global computer vision market is expected to reach USD 19 billion by 2027, up from just USD 11 billion in 2020.
- Computer vision is a sub-domain of Artificial Intelligence that trains computer systems to interpret visual information.
- Computer vision is contributing hugely to the growth of logistics businesses, expected to become a market worth $19 billion by 2027.
- Goods and package dimensioning, product tracking, and load monitoring are the primary use cases of computer vision in the logistics industry.
- To deploy computer vision software in a logistics business, it is ideal to include edge devices and smart sensors in the system to improve the efficiency of process automation further.
Table of Contents
What is Computer Vision?
Computer vision is a sub-domain of artificial intelligence that trains computer systems to interpret visual information. The technology uses machine learning and deep learning algorithms to interpret visual data into binary information, pick similarities and differences among many images, understand the clustering underneath varied categories of images, to learn to identify them without human supervision.
The inspiration for the technology of course comes from optical systems in biological beings. Our brains are capable of identifying and categorizing objects at the same time we see them. You can notice the difference between a ripe and spoiled apple with their tell-tale signs, signs that are visible to your eyes. Now those tell-tale signs have been learned by our brains through our interaction with the outside world and experience.
The same is true for machines with computer vision. The only difference is the computation power of the mechanical systems, which are way higher than our brains. Computer vision software for logistics businesses can be trained on a huge volume of data in a matter of a few minutes. With data modeling, labeling, and variable tweaking, the computer system can learn to categorize the images and make cognitive decisions about them.
For example, a basic surveillance camera can be embedded with an edge device incorporated with computer vision to detect empty spaces. This technology is so simple and easy to integrate with a variety of legacy systems that it has created numerous opportunities to implement computer vision in logistics.
How Computer Vision is Solving Major Challenges in Logistic
Like every industry, there are some time-proven challenges that logistics businesses have always been struggling with for ages. Be it an inaccurate delivery timeline, traffic-induced delays, in-transit product damage, or workplace safety, many logistics companies deal with these issues on a regular basis, and here we have described how computer vision in artificial intelligence software can provide a long-standing solution.
Also Read: The Future of AI in Software Development
Overstocking and understocking have long been huge issues for both supply chain and logistics departments. While automating the supply and demand orders and using predictive analytics can resolve many of these cases proactively, a sense of inaccuracy always looms over the administration. Hiring a computer vision company to develop tracking software for the flow of products can prove to be a game-changing strategy.
Simply integrating the inventory system with the existing camera infrastructures, ERPs, and visual AI edge devices, entire supply chain operations teams can get accurate insights over supplies and inventory in multiple facilities. Thus, visual data can also be categorized to automate the quality assurance of products and accurate tracking.
Docks and Parking Occupancy
One of the major challenges that logistics businesses have constantly faced is inefficiency in parking-related processes. Often jams and loading and unloading parking spaces occupancy can lead to delays in deliveries and consequent lag in the workflows.
Through applications of computer vision in logistics, simple surveillance cameras can be used to detect available parking spaces for logistics transportation. A network can further be built to minimize the cases of traffic jams further improving the efficiency of transportation movement. On scaling, this network can also be integrated with smart city applications to minimize delays, plan transportation and delivery timelines in advance, and manage yard and dock spaces with improved optimization.
Computer vision is one of the most beneficial technologies for automating many warehouse management processes. Simply using images of products, packages, containers, and data from supply chain systems, computer vision software can automatically categorize what goes into the warehouse and what comes out. The precise locations of every element, its movement across the premises, and positioning on different bays can also be automated. This will bring more accuracy and efficiency to the warehouse management procedures, consequently improving the net revenues.
While these regular procedures have the huge utility of computer vision, this still isn’t the most critical area in warehouse management. Processes related to loading and unloading trucks require constant monitoring to ensure both quality and compliance with safety regulations. By automating this inspection via computer vision software, minimal human interference can be ensured to prevent lost-in-transit and theft instances. The system can automatically identify and track RFID tags, flagging cases like tag losses, misassigned tags, erroneous tags, etc., to minimize the cost of replacement tags without compromising on the flow of supply.
Goods and Package Dimensioning
For ages, the goods and packages dimension has created a huge demand for labor and still led to most wastage for the logistics and transportation industry. By using depth cameras based on time-of-flight (TOF cameras), even palletized goods can be dimensioned with unparalleled accuracy. This doesn’t even require too much data and information or a complex computer vision model.
A single image frame of a single unit and automated supply and packaging data from the distribution system are enough. To further improve the efficacy and efficiency of the system, 3D stereo cameras can also be used to implement such solutions of computer vision in logistics. This would help in managing dimensions at all levels, further reducing the cases of mismatched packaging. Using this high-standard hardware by itself results in greater ROI since they bring huge resource optimization; the cost savings can further be improved by deploying custom computer vision development on data from simple surveillance cameras from different angles and points.
Post-pandemic, most distribution centers were under a heavy workload. The massive increase in online shopping resulted in huge demand for manual labor to complete more orders in the given timeline. This way, companies were, on the one hand, struggling to fulfill the orders, and on the other, they were losing more control over the transportation assurance processes. This resulted in increased instances of product damage, misutilization of transportation resources, and delays in the delivery timeline. This further increased the stress on the logistics department of the companies, making situations worse by the day.
Through custom logistic software development, companies can monitor and manage the shipment process with greater ease and accuracy. The system can automate various processes to speed them up and make up for the lack of labor. Edge devices and cameras can be deployed on loading trailers, inventory warehouses, and unloading transports. These devices can closely coordinate with each other, ensuring maximum utilization of transportation resources. The load can also be monitored to deflect any possibilities of supply damage.
Predictive Equipment Maintenance
Predictive maintenance is one of the most lucrative use cases of AI in logistics and transportation sectors. In logistics, too, it is known for generating huge cost savings. Through edge devices in computer vision that can detect excessive thermal output, lag in work, etc., can be collected. This information can then be verified through IoT smart sensors to generate collective reports.
This way companies can detect even the easy-to-overlook changes in the functionality of equipment like conveyor belts, loading machines, packaging robots, etc. Then an alert can be sent across the network to find and verify the source for creating the given change. If there is indeed a requirement for repair, the team can be deployed without wasting any time. Otherwise, replacement of products can be initiated, automating even the purchase order from the parts suppliers. This method of computer vision in logistics is far more efficient and cost-effective than regular and reactive maintenance, and thus it saves a huge amount on equipment depreciation.
Read More: RPA in Logistics and Transportation
Computer Vision for Infrastructure Security
Once a logistics business has deployed visual AI in its SCM processes, it can implement it further to improve its infrastructural security with minimal investments. Surveillance cameras can be fitted in logistics warehouses, places with staff-only access, docks, and parking spaces. Here facial recognition software can prove to be a cost-effective solution, which can ensure that only authorized people are accessing confidential locations.
Other than facial recognition, the visual AI here can also be used to improve workplace safety. Workplaces with risky workflow can be monitored in real-time for threats. This way, an alarm can be raised before any sort of harm can happen either to the workforce or the equipment.
Implementing Computer Vision in Logistics
With logistics, deploying a computer vision system can be considered a complex task. While on the software end, the machine learning model needs to be trained with a huge number of images. Moreover, since most logistics businesses have very different workflows and hence requirements with vision AI, these models need to be developed and trained with those tailored requirements in consideration. Here is a more detailed approach to accomplish the implementation in a sophisticated way-
As with any application of AI, the primary step in the implementation of an AI-based computer vision in logistics is to collect lots of visual data. This can either be in the form of images or video clips. For example, for a quality assurance model, many images of both good quality products and bad quality produces are to be fed to the algorithm.
Labeling the Data
Now simply putting data into the training model isn’t enough. The system must know how these images differ from one another. Here categorizing and then annotating the visual data become the crucial next step. In the above example, both kinds of images must be defined to the system under different categories based on different quality benchmarks.
Train the Model
Once labeled data is there in the system, computer vision developers can create a model with this base knowledge. Here, algorithms like Brox, TVL-1, KLT, and Farneback can be used to extract differentiating qualities of different images and then cluster them based on similar features.
Deploying On Edge Devices
This is the stage where the hardware part of computer vision (aka IoT and edge devices) comes into play. Capturing devices must be placed in real-life locations, and edge devices must be integrated to analyze the data in real-time.
Visual AI Analysis
With data being fetched and categorized based on the aforementioned model, the final output can be sent to the administrators to perform the final visual analysis. Here, a high-performance computation network can further test the categorized data. Categorized data from different locations, thus collected in a centralized server, can further be evaluated for its output quality and the corrective procedures relaying.
Once created and deployed, the computer vision system in logistics can automate processes like quality assurance, load monitoring, network clearance, etc.
Computer vision as a sub-division of AI offers a more ‘human-like’ approach for optimizing logistics operations. From making packaging and loading more efficient to monitor the quality of products, custom computer vision development solutions for logistics are making the industry more lucrative by the day. The unmatched speed of the processes and the absolute accuracy contribute to huge cost savings enabling the sector to scale without additional expenditure.
Computer vision in logistics often works with edge computing and IoT. This is why, when hiring a computer vision software developer, it is important to ensure that they are proficient in working with the remainder of the technologies. In case you’re giving your entire project to custom enterprise software development company, it would be ideal to see if they have teams for planning the hardware requirement for the system implementation.
Matellio is one such engineering studio that has both skills and experience in all three technologies. Our experience in creating custom solutions for logistics gives us more edge to deliver success. If you have an idea or requirement for your logistics business digitalization, all you need to do is fill out this form. Our experts will reach out to you to discuss your project requirement in detail and create a custom computer vision software development plan along with a free quote.
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