Couldn’t Attend Our Webinar? Here are Top 3 Takeaways You Don’t Want to Miss

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Industry 4.0 solutions like computer vision (CV) technology transform manufacturing operations, automate quality control, eliminate process inefficiencies, improve worker safety, and ensure regulatory compliance.

Computer vision technology uses deep learning AI models to capture and process visual data and deliver actionable insights. Sophisticated IoT and AI technologies can enable computer vision-aided machines to interpret images and notify users of necessary actions.

If needed, it can also act without human intervention.

Prime Computer Vision Use-cases in Manufacturing Automation

At Gramener, we combine computer vision with technologies like geo-spatial AI, NLP, and text analytics to deliver specialized solutions. The key computer vision techniques used across all use cases are object detection, object tracking, image classification, image segmentation, text detection and extraction (OCR), etc.

We’ll highlight some well-known use cases involving computer vision applications in detail below.

Also, feel free to check out how our computer vision solutions have helped our clients.

Must Read: 22+ Computer vision applications from several industries to serve your business better.

Production Assembly Line Defect Detection

Computer vision can help automate the continuous assessment of manufacturing assembly lines. Cameras set up at strategic locations capture images and upload the data to a cloud server. Anomalies and defects are identified throughout the assembly line and classified based on type and severity.

Floor managers are notified through IoT-enabled alerts in the event of any unusual production activity. They also receive analysis reports and actionable insights.

Application of computer vision technology in product defect detection can improve quality control accuracy by up to 99.9% and help cut labor costs by around $49,000 per production line.

Drug Defect Detection

Computer vision can help increase productivity, standardize product quality, and assure regulatory and legal compliance. Convolutional neural networks can help detect drug defects with an accuracy of around 94%.

Cameras capture the images of the product and upload to the server. Computer vision algorithms analyze these images to identify and classify product defects. In the event of any unusual activity, it can notify the factory managers. Find out more about computer vision in healthcare.

Packaging Defect Detection

One industry where computer vision technology is helping to improve defect detection in packaging is pharmaceutical drug manufacturing.

Cameras are set up throughout the assembly line at strategic locations to capture images and upload the data to a cloud server. When anomalies are identified, the products classified as defective are held back.

Machine learning has helped achieve product packaging defect detection rates of more than 98%.

Create a Safer Workplace

46% of the participants in an annual EHS Daily Advisor survey identified employees taking shortcuts or ignoring rules as one of the top three safety challenges.

In an industrial setting, when workers don’t wear personal protective equipment (PPE), it can lead to an increase in workplace injuries, accidents, or even deaths. Computer vision technology can prevent accidents by monitoring workers to ensure they wear their PPEs.

Automated AI systems such as computer vision can detect even minor violations, such as removing a helmet or face mask. It can help report and correct them, resulting in an overall improvement in operational efficiency.

Webinar Takeaways

On 14th September 2022, computer vision experts from Gramener discussed the applications of the technology in the manufacturing industry.

The expert panel comprised Sundeep Reddy Mallu, Head of ESG and Analytics; Sunil Kardam, Head of Logistics and Supply Chain; and Haritha N, Senior Manager, Data Sciences at Gramener.

The following are the salient takeaways from the hour-long webinar from the panel of industry veterans.

Timeline to Build a Computer Vision Solution and Dependencies on Clients

Typically, we can deliver the pilot within two months. The dependencies on the client are relatively simple – a camera and a place to install or mount it, stable and fast internet, and data storage capabilities.

With the required infrastructure, the object images can be quickly captured in an automated fashion. The idea is to capture sufficient images (roughly 200-800) covering all the various types (e.g., defects) that can subsequently be fed into the analytics model.

The next step is data annotation or labeling so that the analytics model learns the proper objects to pick. The model’s output can again be fed back into the model to improve the accuracy and utility.

Technologies Used for Building a Computer Vision Solution

Computer vision technologies comprise object detection, object extraction from images, text extraction, and text detection.

For AI algorithms, from a base framework standpoint, our go-to preference is Py Torch. In terms of the architecture that we use across the board, our go-to preferences are Yolov7 and ResNet, followed by VGG 36 in terms of building the models.

For all model versioning and tracking, MLflow is preferred, followed by cube flow, from a model construction building standpoint.

For the final leg, data pipelines and model monitoring, we primarily rely on DVC. It is the best open-source, algorithm-based framework that is easy to report and track.

Hardware Investments for Equipment Used in a Computer Vision Solution

The investment in Cameras is minimal. The cost of installing a two-megapixel resolution camera can be as low as $70. For $200, you can get a high eight-megapixel camera for edge locations.

Pushing the captured image to a cloud location poses a challenge. This is where IoT-based edge computer solutions come into play.

Using a Wi-Fi connection, you can use advanced incremental capabilities to capture images and stream them to a central location.

In the case of videos, you can route the stream through a DVR setup into a streaming service like Kafka. Installation costs of the camera and equipment are use-case dependent.

Conclusion

Computer vision technology enjoys myriad applications in manufacturing automation and is expected to grow continuously for the foreseeable future. Its defect detection use cases have a stronghold in the pharmaceutical industry, packaging, and more, delivering high detection and accuracy rates.

It can also improve other facets of industrial operations, such as workplace safety. It is easy to set up, requiring cost-effective hardware installation with minimal dependencies on the client.

Connect with us if you want to transform your manufacturing or production operations with AI automation.