Computer Vision Services

What is Computer Vision?

Computer vision is a set of techniques that allows computers to understand what’s in the image and video content. Its aim is to extract structure, meaning, or information from pixels. Historically, early computer vision systems used rule-based techniques like edge detection, blob analysis, color histograms, feature matching (SIFT, SURF, HOG, etc.). They scaled poorly, however. Modern computer vision relies on machine learning methods which offer greater accuracy, performance, and most importantly, generalization.

Computer vision is now used as technology that is used to spot objects, detect defects, find anomalies, read text and decode labels, and “give sight” to equipment. It’s quite effective at automating repetitive tasks and aggregating insights for predictive analysis. Yet, it requires training to do so, and this is where computer vision services come in.

In industrial contexts, computer vision is used to: 

• Spot defects or anomalies during the production process. Find out more about defect detection.
• Detect and classify products, parts, components on assembly lines.
• Read text, labels, barcodes with optical character recognition.
• Monitor worker safety and ergonomics via pose estimation and behavior detection.
• Enable automated assembly, sorting, and more by guiding robots and machinery.

Each computer vision solution tailored by DAC.digital for the industrial environments helps manufacturers tackle challenges they face at specific points of the production process, from quality control to assembly and logistics.

What Computer Vision Service Do You Need?

Share a few details about your challenge or idea, no matter how early or complex. Our expert team will reach out and help you shape it into a solution for scale it the way it makes business sense.

What do you need to develop computer vision?

For industrial computer vision development, you need to think about hardware, software, data, and infrastructure that you plan and integrate according to the task, environment, and business requirements. Let’s discuss it.

Hardware requirements

The hardware forms the foundation of any CV system. To help you imagine what may be needed for your system, we will use a recommendations that we gave to one of our clients:

• Camera: Industrial-grade camera (e.g., 12–20 MP, Sony IMX series) for high-resolution imaging.
• Lens: Macro or telecentric lenses to ensure accurate, non-distorted dimensional measurements.
• Lighting: Structured, multi-point, or diffuse lighting to minimize shadows and reflections.
• Processing device: Industrial PC or Edge AI module (e.g., NVIDIA Jetson AGX/Orin) for on-site inference.

Your system may also require this:

• Integration devices: Optional robotic arms, drones, or conveyor interfaces.
• Monitoring: Dashboards for operators to visualize results.

Software and system infrastructure

A complete CV solution requires more than just cameras and processors. It also needs:

• Image capturing and preprocessing software: Ensures consistent data quality and standardizes images for model input.
• Software: Implements CV algorithms for classification, detection, and measurement.
• Data handling infrastructure: Stores, organizes, and streams images to training or inference pipelines.
• Model training frameworks: Tools like PyTorch, or TensorFlow for training deep learning models; ONNX for cross-platform model deployment.
• Deployment and serving layer: Optimized models are served via local edge devices or cloud endpoints using frameworks such as FastAPI, TorchServe, or TensorRT/OpenVINO for accelerated inference.
• System testing and calibration: Ensures that hardware, software, and models are aligned and validated under real production conditions.

Data requirements

Computer vision models are not universal; they must be trained on task-specific data to perform reliably. Key considerations include:

• Data management: Use cloud or on-premise storage, versioning tools like DVC or Git LFS, and ensure datasets are reproducible and scalable.

• Data collection: Gather representative images from the production line covering all variations (lighting, orientation, defect types, etc.).

• Data annotation: Label images with bounding boxes, segmentation masks, keypoints, or class labels using tools like CVAT or VGG Image Annotator.

Model training and deployment

Once data is collected:

• Data preparation: preprocessing, augmentation, setting reasonable data splits, including a class balancing study
• Model training: Train the model on labeled datasets, leveraging transfer learning where possible to reduce training time and improve performance.
• Validation: Evaluate using metrics such as precision, recall, F1 score, IoU, and mAP.
• Deployment / Inference: Models are deployed on edge devices or in the cloud. Inference pipelines can return classifications, bounding boxes, segmentation maps, or alerts depending on the use case.

Imagine the following scenario to visualize how a computer vision system may look. In a production line, a camera captures each product and sends it to an inference endpoint (locally or cloud-hosted). The CV model evaluates the image, flags defects, and logs the item for review.

You don’t have to do it alone. At DAC.digital, we partner with our clients to design, implement, and maintain end-to-end computer vision solutions. Ask us for help.

What can computer vision development experts help you with?

Hardware selection

You might know what task you want to solve, but not which equipment to get. Get a partner who can guide you through hardware selection and can recommend the right cameras, lenses, lighting, and that will work in your production environment as well inform you about edge devices necessary for your project.

Data strategy

Do you want to learn what your dataset should consist of? Let’s work together! Find out how to collect and annotate data, learn about augmentation techniques, and create a golden dataset, so your model learns exactly what it needs to succeed in production.

Model training

Get expert assistance in designing and training task-specific models that are efficient to train and avoid mistakes like not including cucumbers (an inside joke that you will learn about during our free AI Project Management Masterclass.)

Model deployment

You may have a model that works in a testing environment, and you need help with full-scale production. Deploy your model on edge devices, cloud systems and on-prem, integrate it with specialized systems and build infrastructure around it.

MLOps

To get a well-performing model in production, you might need MLOps that handle your infrastructure, continuous integration and delivery, versioning, and monitoring.

At DAC.digital, we don’t just build custom computer vision solutions. We also love sharing what we know.

Whether it’s through hands-on workshops or insightful webinars, our team is always eager to connect, collaborate, and exchange ideas.

We believe innovation thrives on open dialogue, so we make sure our experts are not just experienced, but also approachable and easy to talk to. They’re here to guide you, answer your questions, and help you explore the full potential of computer vision.

Watch a webinar we recorded for manufacturing industry.

Got a Computer Vision Development Project in Mind? Let’s Discuss It.

Contact us!

Send us an email: [email protected]