Imagine a team of inspectors which can monitor defects for every manufactured item with laser-point accuracy, 24-hours a day. This team never gets tired, never gets distracted, and works for a fraction of the cost of human quality control inspectors.
Moreover, this team can instantly scale to handle inspection tasks of any size or complexity. This team doesn’t have to be imagined, as this high standard of defect detection is already available to manufacturers, and it does not require human inspectors, nor months of development.The team is not a team at all, but rather a sophisticated machine vision system for defect detection.
This computer vision technology uses a network of cameras, edge devices, with highly customizable AI models to help manufacturers eliminate their quality inspection bottlenecks, dramatically reduce costs, and produce more and safer products faster – all while meeting and exceeding their quality control standards. With new computer vision platforms, the customization of AI models is now making these deployments ever more flexible.
In Quantifying the Full Costs of a Product Defect, Alec Baker identifies the following key ways that product defects cause enormous costs for manufacturers and businesses:
Considering the tremendous financial costs of defective products, the ability to identify defects quickly and effectively before they leave the manufacturing facility can bring enormous ROI benefits. However, due to the limitations of human quality control staff, there is only so much that manufacturing facilities can do. A certain number of defects will invariably go undetected, and potentially trigger some or all the above-listed costs.
Manual visual inspections by humans have long been required to ensure that manufactured products meet the highest quality control standards. These human inspectors were traditionally required to visually examine details like color, texture, shape, quality, position, labeling, etc. in industrial and CPG manufacturing settings. However, human visual inspectors come with several downsides and disadvantages:
The boring and monotonous nature of visual inspection tasks is particularly concerning. The longer human workers try to focus on mundane visual tasks – such as looking for misplaced bottle caps, monitoring live video feeds, checking for painting errors, or watching for broken parts – the more bored, fatigued, and error-prone employees become. Research shows that work-related boredom brings a host of negative consequences for both employees and employers, such as:
Despite the above negative outcomes for employees and employers, the use of human quality control inspectors is a vital part of ensuring quality in manufacturing operations, and especially important in precision manufacturing. Nevertheless, the error-prone nature of human quality control inspectors – and the rising cost of hiring and training new inspectors – creates a serious bottleneck when it comes to achieving fewer defects, better automation, and greater scalability of manufacturing processes.
Advanced visual AI technology can eliminate the errors, inefficiencies, and costly consequences associated with employing human workers to complete visual inspection tasks. By overcoming the human limitations associated with completing these tasks, computer vision for defect detection achieves the following ROI benefits for manufacturers:
Computer vision systems for defect detection use strategically placed high-resolution cameras to capture video or image data of manufactured items. They then use sophisticated AI models to evaluate the visual data and deliver instant insights, metrics, and defect alerts to key employees and decision makers. This allows the end-user to identify and replace defective products more efficiently and successfully, thereby avoiding the costs and workflow inefficiencies that manufacturing defects cause.
Every visual AI system requires a “base training” – a process that involves capturing hundreds of thousands of annotated images. By using advanced tools to automate most of the image/video annotation process, end users can quickly provide the visual data that the AI models need to distinguish between a “defective” and “non-defective” product while identifying the various types, classifications, and severities of defects. Later, end users can rapidly finetune the training of existing visual AI models with additional data for new and different use cases.
When using the Chooch Enterprise Platform for defect detection, end users can generate a custom, lightweight visual AI model and deploy it to the cloud or to local “edge devices” at the manufacturing facility. End users can manage their deployments on existing high definition cameras through a cloud-based dashboard. Scaling these systems to other facilities and updating the models to apply to slightly different use-cases is a fast and straightforward process.
As a final note, deploying visual AI defect detection systems to a local edge device offers several key advantages. First, it allows all data and processing to stay onsite without touching the internet, so there are fewer risks of data breaches and greater security. Second, for mission-critical, 24/7 applications, edge devices offer greater resilience, better bandwidth, faster processing, and a reduced risk of downtime. On the contrary, there are many situations that do not require a high degree of data security, bandwidth, or speed. In these cases, cloud-based deployment may be sufficient to meet the manufacturer’s defect detection needs.
The flexibility and scalability of the Chooch Enterprise Platform means that clients can deploy and redeploy visual AI defect detection strategies in a wide variety of industries – by making quick adjustments to pre-existing models. The following use case examples illustrate the versatility of Chooch’s computer vision solutions for defect detection in manufacturing.
The table below shows additional use cases for visual AI defect detection in the manufacturing industry:
Visual AI Use case for Defect Detection
|Nonferrous Metals||Wires, cables, aluminum, stainless steel||Scratches, cracks, dirt, dents|
|Building Materials||Wood boards, sashes, metal fittings, tiles, other materials||Scratches, cracks, surface defects, dents|
|Electronic Parts||Electronic materials, electronic components, circuit boards, electrical panels, other items||Scratches, chips, cracks|
|Auto Parts||Material parts, resin parts, fabrics, other materials||Scratches, dents, dirt, cracks|
|Raw Materials||Chemical fibers, rubber, glass, paper, pulp products||Scratches, cracks, dirt, dents|
|Food||Processed foods, beverages, food packaging, bottling||Foreign objects, labeling errors, leaks, packaging damage, missing bottlecaps|
|Medical||Pharmaceutical medicines, medical devices, surgical equipment, wound dressings, syringes, other items||Foreign objects, labeling errors, cracks, defects, dirt, impurities, sanitary issues|
Beverage factories need to monitor and inspect their production lines for misplaced bottle caps. Due to the human limitations of quality control inspectors, they need to have several tiers of inspection controls beyond real-time inspections as bottles pass through the production process. These controls typically involve additional human inspectors reviewing video footage of bottles to identify cap placement errors.
These secondary quality control measures are far from perfect. Often, by the time they find a misplaced bottle cap, the defective bottle has already left the factory. The tremendous efforts required to fix the problem can result in the destruction of 1,000 more bottles of product, additional transportation costs to return and replace the bottles, and other logistical costs.
Why does a single bottle cap defect result in so many costs? For most beverage manufacturers, it is less expensive to dispose of an entire lot of product than to try to find the individual box – among countless boxes – that contains a single defective bottle. Product manufacturers may also need to recall delivery trucks, ask retailers to hunt for defective products and incur other costs related to tracking down the misplaced or missing bottle cap.
The problem gets worse if the secondary controls fail to identify the defect before the bottle makes its way to the customer. One customer photo of a moldy, capless bottle can go viral on social media and result in lasting reputational damage, reduced sales, and increased marketing/PR expenses related to rebuilding consumer trust in the brand.
With the Chooch Enterprise Platform for defect detection, CPG and industrial manufacturing businesses now have access to advanced defect detection technologies that far exceed human ability – and they can develop and deploy them in a matter of weeks. Chooch visual AI models (running on locally installed edge devices) capture and review live video from cameras installed along production lines. Then, the AI models instantly interpret the visual data, sending their conclusions, reports, and defect alerts to staff who can pull defective products from the factory line before they are packaged into boxes.
A large-scale aircraft parts manufacturing and/or aircraft repair operation needs to employ expert mechanics to visually review several points of inspection for specific aircraft parts to ensure the proper functioning of equipment and identify potentially dangerous defects. It is not unusual for the visual inspection of a single aircraft part to require the following:
When computer vision technology performs aircraft part inspection tasks, no human labor hours are required. In contrast to the above cost and efficiency figures for employing a team of human inspectors, here is what a visual AI inspection system can achieve:
*Based on theoretical projections.
Comparing the cost of using visual AI to the cost and efficiency of human inspectors, the ROI benefits are clear. A single part inspection by human mechanics requires four skilled employees working for 2.5 hours each at a price of $100 each per hour. This comes to 10 human hours and a total operational cost of $1,000 per inspected part. Meanwhile, a computer vision model for defect detection can complete the same part inspection task in 0.2 seconds without any human labor.
Chooch’s computer vision platform allows end-users to manage pre-trained visual AI models for defect detection – and the use-cases are practically endless. These pre-trained models are available now for immediate integration into manufacturing workflows. They can provide real-time defect detection insights, reports, and alerts for the following use-cases:
As for compatibility features, the Chooch Enterprise Platform generates inferences that consist of a .json and .jpeg file. These inferences are compatible with virtually any business analytics platform, on-premises, or cloud based system through API or MQTT broker. The Computer Vision platform can also notify key personnel and decision makers through text message and email alerts as soon as a defect is found.
Finally, the Chooch Enterprise Platform includes an easy-to-use dashboard with the following features:
Founded in 2015, Chooch is a Silicon Valley-based computer vision company that offers a ready-now visual AI platform. Chooch solutions accurately detect and evaluate objects and actions in images and video for a wide variety of use cases – including defect detection, diagnostic medical analysis, PPE safety monitoring, employee/visitor fall detection, fire/smoke/heat detection, maintenance/repair inspections, and many other scenarios.
Deployable in the cloud or on the edge, the Chooch Enterprise Platform generates data sets, rapidly trains new models, and provides end-users with instant and accurate inferences. Chooch solutions have been successfully deployed in the widest range of verticals, including the U.S. government, aviation, media, geospatial, healthcare, and security/IoT. Chooch enterprise partners include NVIDIA, Deloitte, AWS, HPE, DELL, Lenovo, and ADLINK.
If you would like to learn more about Chooch and its range of Visual AI solutions, please visit www.chooch.com for more information.