Inspecting equipment, products and materials with the human eye is the oldest and simplest form of visual inspection. It is still used today in manufacturing, the energy industry and the medical field because it is effective for detecting surface-level defects.

In the pre-digital era, inspectors were trained to identify defects, sometimes with the naked eye, and in other cases, by using the simplest of tools, such as lights and magnifying glasses. With the advancement of portable, high-quality cameras and drones, visual inspection has evolved to a new stage.

Today, companies collect digital images and videos of machinery, manufactured products and other aspects of physical operations to conduct visual inspections. Inspections with video footage and imagery can be done in real-time from a remote location or reviewed later once the camera collecting imagery has been retrieved.

Software that uses artificial intelligence (AI) is also used today for visual inspection automation. By “teaching” a computer to read images and determine when they meet acceptable standards, companies can automate the visual inspection process, saving time and sometimes, improving accuracy. This might range from identifying corrosion on the tops of wind turbines to identifying faulty connectors within products’ electronics.

One example of integrating AI into visual inspection systems is in the automotive industry. Today’s car manufacturers use images and deep learning to quickly and consistently identify defects earlier in the production process.

With this technology, also known as intelligent visual inspections, organizations can conduct inspections faster, more accurately and cost-effectively across a wide range of environments. By employing machines to conduct visual testing, companies can keep people out of hazardous areas and confined spaces, such as storage tanks, protecting the safety of workers without sacrificing the benefits of visual inspection.

Visual inspection is a form of non-destructive testing (NDT). Nondestructive methods allow inspectors to assess a system or component without permanently changing it.  In addition to visual inspections, NDT also includes inspection techniques such as emissions, radiographic, X-ray and infrared, and ultrasonic testing.

NDT is a term often used in manufacturing or industrial operations; however, it can apply to several other industries. For example, an X-ray to assess whether a person has a broken bone or a proofreader reviewing a document and indicating errors that need revisions would also be types of NDT.

Because visual inspections look only at the surface, organizations will often use them in tandem with other testing methods.

Every industry and organization has its own process for conducting visual inspections. Yet, there are commonalities within the inspection workflows often found across visual inspection processes. These include:

• Identifying all equipment, materials, products and infrastructures that need to be inspected.
• Defining which conditions should trigger an inspection.
• Creating clear guidelines as to what constitutes a defect.
• Noting how often these inspections should be performed.
• Creating a means for reporting, documenting and addressing defects and downtime when detected.
• Incorporating visual inspections into maintenance checklists.

Once a process has been established, organizations might use various methods to carry out visual inspections, including:

• Random sampling. Quality checks are performed on randomly selected products or physical assets. In manufacturing, products are often checked right at the production line for obvious visual defects. 
• Full manual sampling. All products are inspected manually by a person trained to identify defects. This can be a physically demanding job with repetitive actions that should be accompanied with safety policies, ergonomic equipment and appropriate tools.
• Remote visual inspection (RVI). Using remote cameras, edge technology and drones, organizations can observe equipment safely from afar. This inspection solution may be conducted in real-time; or in remote areas where connectivity might be an issue, the inspections can be performed by retrieving the images and analyzing them later.
• Automated visual inspection. Products are inspected in real-time using cameras, image-processing methods and machine learning algorithms. Unlike RVI where teams take inspection equipment into the field, automated visual inspections are typically done onsite in one location.

Visual inspection has been used for many years to ensure quality and safety. In addition, it also offers these benefits:

• Savings: By identifying defects as early as possible, companies can reduce the costs of scrapping defective products or identify assets that need repair faster.
• Safety: RVI lets companies perform visual inspections safer than ever by helping them identify defects in environments that might be harmful.
• Optimization: Visual inspections are a quick, inexpensive and non-intensive way to assess quality. When using automated visual inspections, organizations can further optimize the inspection process by reducing hands-on time.
• Speed: When using automated visual inspection, the inspections are performed faster than with human workers and can occur anytime, 24-7.
• Accuracy: Automated visual inspections can be more accurate than inspections with the human eye, because they can catch slight defects that are imperceptible or easy to miss.

When quality control and safety are paramount, visual inspection is used, and may be required, including in these use cases:

• Manufacturing: Whether manufacturing cars, pharmaceuticals or semiconductors, visual inspection identifies assembly and cosmetic defects on the manufacturing floor.
• Healthcare: From manufacturing medical devices to inspecting equipment before surgery, visual inspections are key to patient health and safety.
• Energy: Visual inspections improve the safety of equipment in many different facets of the energy industry, from mining and fuel extraction to power generation.
• Civil infrastructure: Inspecting roads, bridges and tunnels for potential issues is mandated for public safety, can take months and requires attention to minute detail.

Until recently, visual inspection was a process that was difficult to automate. Computers had not yet caught up to the naked eye. Yet, the latest advances in AI capabilities have made automated visual inspection features more efficient and accurate.

A major hurdle that engineers had to overcome is a computer’s inability to process the contents of an image. Computer vision helped solve this problem. This process enables computers to derive meaningful information from digital images, videos and other visual inputs. Not only can computers now process the image, but they can also generate data insights that can be used to take corrective actions or make recommendations. 

Here are some capabilities computer vision offers:

• Image recognition: This enables computers to draw context and meaning from an image, including identifying objects, places, people and handwriting.
• Object detection: This technique identifies and locates objects within an image or video by drawing boundaries around the objects, allowing for closer inspection within the context of the object.
• Remote monitoring: Much like remote visual inspection, remote monitoring reviews and inspects an object via an image or video, either by a human or by using AI-powered technology.
• Predictive asset management: Organizations can take data from machines and assets to understand the asset’s total health in terms of its lifecycle and then leverage this data to predict when a failure might occur.
• Worker safety alerts: When an unsafe situation is detected within a confined space or other controlled area, the systems can alert workers of potential risk.