14 Computer Vision Applications in Manufacturing for 2025

One standout application of surface defect detection in industry is OkKast built specifically for precast concrete manufacturing. OkKast uses images and machine learning algorithms to detect surface defects.

What makes OkKast unique is its integration with BIM data: it compares what’s built against what was planned, flags any defects and stores visual records for each element. This eliminates the need for manual inspection, reduces rework, and ensures full documentation of surface quality.

Not all defects are visible on the surface. Some flaws, like weak spots in materials or structural issues, can only be found using advanced testing methods. Computer vision can also help here.

Radiographic Testing (RT)

Radiographic Testing works like an X-ray at a doctor’s office. It sends radiation through a material, and sensors create an image showing any hidden defects, like cracks or air pockets. Computer vision can analyze these X-ray images in real-time, so workers get notified about defects while the product is still on the plant floor.

Resonant Testing

Resonant Testing checks for defects by making an object vibrate and then analyzing the sound it makes. A perfect part will produce a predictable vibration pattern, while a defective one will sound different. Computer vision can learn the normal sound patterns and quickly detect when something is off, so manufacturers can locate internal stress points or cracks.

Thermographic Testing

Thermographic testing uses infrared cameras to measure heat flow in a material. When heat moves through a part, internal defects will change the way the heat spreads. Computer vision can analyze thermal images to quickly identify problem areas, even in complex or large structures.

Foreign Object Detection, FOD for short, in manufacturing means finding unwanted particles that could affect product quality and cause health risks. These objects can be things like metal shavings, plastic bits, or even flies in liquid. Popular methods of FOD in manufacturing include Computer vision using input from cameras or X-ray scans, infrared sensors, and more.

This equipment might seem intimidating. If they are so – recent studies indicate that even smartphones can be utilized for FOD. A scientific paper about seaweed harvesting foreign object detection demonstrated that even smartphones can effectively detect foreign objects when paired with appropriate software.

Computer vision helps factories stay safe by continuously observing the environment and ensuring workers follow safety guidelines. AI-powered cameras detect workers without proper protective gear, monitor restricted areas, and identify hazards like blocked emergency exits.

There are many ready-made computer vision solutions for safety monitoring. One of them is Intenseye. For manufacturers who have sophisticated factory floors, lack space or need special integrations, we recommend custom systems that we can make. Please check our services for the manufacturing industry.

Traffic Analysis

Computer vision can track traffic in the manufacturing site. When paired with IoT sensors and machine learning, manufacturers can use computer vision to inspect layouts, recognize patterns in movement. In result, it can reduce congestion and prevent accidents.

Environment Monitoring

Computer vision can analyze thermographic data to check temperature and notify workers about overheating of a certain equipment (predictive maintenance). Other sensors can be used to measure air quality and detect hazardous conditions, such as detecting spills or signs of fire risk.

Posture and Fatigue Checkers

Worker fatigue and poor posture can lead to injuries and decreased productivity. On-site cameras with inspection systems analyze body movements to detect signs of fatigue or improper posture. These systems can alert supervisors when a worker needs a break or suggest ergonomic adjustments to prevent long-term strain.

Source: One of the pose detection system that we did for a healthtech startup.

Leak Inspections

Leaks in pipelines or machinery can cause significant safety hazards and production losses. In this case, an infrared imaging vision system can detect leaks in real-time, even in hard-to-reach places for a regular worker.

Collision Detection

Another use case of computer vision is collision detection. Cameras track the movement of people and machines and alert of any collision that’s taking place. Manufacturers can also use data from computer vision and build collision prevention systems that predict when collisions are about to happen.

Traditional barcode scanners use lasers to read barcodes. Computer vision helps analyze barcodes and it is able to tell if it’s the right barcode for quality control, update inventory, and send it to other systems for data analysis.

Computer vision can make sense of damaged, blurry, or poorly printed barcodes, something traditional scanners struggle with. It also works with multiple barcodes at once to speed up operations.

Barcode scanners are one of the easiest projects for OpenCV enthusiasts (here’s a quick tutorial on building a barcode reader with OpenCV). And they are a quick computer vision project for introducing AI for quality control if you want to start one.

Robots in manufacturing are automated machines that perform tasks like assembly, welding, packaging, and quality inspection. They handle repetitive or hazardous jobs. They have been used in factories for years, but computer vision “gives them eyes”, so they can work independently.

Computer vision-backed industrial robots are still emerging, but they’re already being tested and used in a variety of ways. One key example is piece picking, where robots can pick up random objects without needing specialized training or programming. This development is significant because it allows robots to perform tasks that were once simple for humans, like grabbing a random object, but difficult for machines.

Cobots, on the other hand, are designed to work alongside humans rather than replace them. Unlike traditional industrial robots, which are kept behind safety barriers, cobots have built-in visual inspection systems that allow them to detect human presence and adjust their movements accordingly.

Watch what you can do with an off-the-shelf robot that’s boosted with computer vision:

Computer vision in manufacturing can be used for automatic sorting because it can make identifying and organizing products faster and more accurate. This technology uses cameras, smart algorithms, and automated sorting machines to inspect products in real time. It helps reduce mistakes, improves efficiency, and lowers the need for manual work. Manufacturers use computer vision to check product quality and sort items quickly.

To make sorting work, three key parts are needed: cameras (and or sensors), deep learning models, and a sorting system (read Roboflow’s article for a detailed guide). Cameras capture images of products while deep learning models help the system recognize patterns and classify products correctly.

Once the model finishes its analysis, the sorting system takes action. Mechanical pushers separate products based on their category. For example, in a factory, the system can remove low quality products from the production line. In recycling, vision systems can sort materials like plastic, glass, and metal. In food processing, they can check the ripeness of fruits and separate them accordingly.

Automated object counting with computer vision is transforming industries like manufacturing, logistics, and retail by replacing manual processes with precise, real-time tracking. As explained in a detailed article about computer vision sorting by Labellerr, the system starts with image acquisition, where cameras capture photos or video of products as they move along the production line. These images are then preprocessed to enhance quality, like removing noise, adjusting lighting, so the AI can analyze them effectively.

Next, the system performs object detection, locating each item in the image. Once objects are detected, object recognition kicks in, classifying them based on predefined categories such as size, shape, or quality. Counting is then executed using techniques like bounding boxes, segmentation, or density estimation to tally the exact number of items.

In some setups, sorting follows, where objects are categorized automatically based on their attributes, allowing for actions like diverting defective items or separating products by type. Finally, the results are output into dashboards or reports and can be integrated with other business systems (ERP, MES) to trigger decisions or automate downstream tasks.

Drones powered with computer vision can fly around the factory and scan barcodes, QR codes, or product labels, identify the right items, pick them up, and transport them to a packing area without the need to manually search for products.

Aside from packages or products, they can pick small parts that are necessary for assembly. Whatever data a drone has can be integrated with systems that workers use in the manufacturing process.

There are many ready-made drones with AI available on the market. One of the best rankings is made by Flypix.ai.

Source: A picture of a drone used in one of our R&D project for tree recognition with computer vision.

Computer vision is improving how factories handle pallets. It helps with three main tasks: verification, measurement, and tracking. Let’s discuss them one by one.

Pallet Verification

Manufacturers can use computer vision for pallet verification. Verification means that the right products are on the right pallets. Computer vision can scan barcodes and labels on pallets as they move through the warehouse. This helps confirm shipments and prevent errors.

Pallet Measurement

Measuring pallets is important for storage, shipping, and cost calculations. Computer vision can help capture the size and sometimes the weight of palletized loads. This removes the need for manual measurement and ensures more precise data. Accurate measurements help companies use space better and avoid unexpected shipping fees.

Pallet Tracking

Another use case of computer vision in manufacturing is pallet tracking which helps factories know where their goods are at all times. Cameras and barcode readers follow pallets as they move through the supply chain. This improves visibility, reduces lost inventory, and makes operations more efficient.

Motion tracking with computer vision helps machines understand and track movement in video or image sequences. It has many applications like surveillance systems, autonomous robotics, and navigation for forklifts and so on. Computer vision can detect and follow moving objects to analyze their behavior. It can even track multiple objects at once when trained to do so.

Another use case of computer vision in manufacturing is making a digital twin. A digital twin is a virtual replica of a physical object, system, or process. Manufacturers can use a digital twin as an application of AI in manufacturing to experiment without having to go into the field or conduct expensive trials on physical equipment. This virtual replica can vary from simply tracking equipment data on a graphic to simulating an entire production line.

Companies can use computer vision to update their digital twin and make a 2D or 3D reconstruction of the product, which is very useful for building constructors, and more.

Manufacturers can also simplify product prototyping with computer vision. This research paper shows how designers can integrate computer vision, robotics, and CAD software to automate 3D model creation from real-world objects. A robotic hand positions the object while a smart camera captures images. Computer vision extracts the object’s shape, refines edges, and transfers the data to CAD software (CATIA) via a macro. A 3D solid model is then generated using extrusion techniques.

Tested on both simple and complex shapes, this system ensures precise dimension extraction and accurate model regeneration. It’s a game-changer for quality control, 3D printing inspection, and reverse engineering, helping manufacturers streamline production and detect defects with minimal effort.

Read this research paper: Integrating Computer Vision and CAD for Precise Dimension Extraction and 3D Solid Model Regeneration for Enhanced Quality Assurance.

Dimensional Tolerance in Manufacturing Quality Control refers to the allowable limit of variation in a physical dimension of a manufactured part—such as length, width, height, diameter, or thickness. It defines how much a product’s size can deviate from its design specification and still be considered acceptable.

Modern computer vision systems and industrial cameras are frequently used for non-contact dimensional measurement during or after production.

Common applications of dimensional tolerance checks include inspecting panel gaps in automotive, PCB trace spacing in electronics, bottle neck width or fill level in packaging, and hole diameters in machined metal parts. These ensure products meet precise specifications across various industries.

Which computer vision application interests you the most? Is it defect detection, safety monitoring, or automated sorting, or something else? If you’re ready to pick, we can create a vision system just for you.

Implementing advanced technology requires expertise. At DAC.digital, we specialize in custom computer vision development that fit your factory’s unique needs. Whether it’s quality control, predictive maintenance, robotics, digital twins, or autonomous drones, we can design, develop, and integrate cutting-edge systems that enhance efficiency, reduce costs, and improve safety.

We build tailored AI solutions for manufacturing that can work seamlessly with existing infrastructure. Our expertise in computer vision, machine learning, and industrial automation ensures that your manufacturing processes stay ahead of the competition. Ready to explore AI for your factory? Contact DAC.digital and let’s build the future together.

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