Machine Vision: Why Implementing It Now Is Easier Than It Seems

Five years ago, machine vision was associated with major auto factories and electronics giants — the technology required expensive equipment, teams of programmers and months of setup. Today machine vision is an accessible tool that a small company can deploy in a week. The democratization happened quietly, but completely.

What changed

Three technological shifts happened at once:

1. Hardware became cheap. A 4K camera with autofocus costs 3,000–5,000 rubles. A depth camera (RGB-D) — 15,000–25,000. An industrial camera with gigabit interface — 30,000–50,000. Compare this with 2018, when a similar set cost 200,000–500,000.

2. Neural networks became available. Pre-trained models for common tasks (face recognition, object detection, segmentation) are available for free. Fine-tuning for a specific task takes days, not months.

3. Cloud computing eliminated the need for expensive servers. Edge computing (on the device) or cloud APIs (Google Vision, AWS Rekognition, Yandex Vision) give access to industrial-level algorithms for pennies per call.

What machine vision does today

In production:

• Quality control: detection of defects, verification of completeness, measurement of geometric parameters
• Worker safety: tracking PPE, detecting dangerous situations
• Process control: tracking parts on a conveyor, sorting, packaging

In agriculture:

• Plant health monitoring (NDVI)
• Pest and disease detection
• Yield prediction, fruit ripeness

In retail:

• Customer flow analytics
• Planogram compliance
• Self-checkout, anti-theft systems

In transport:

• License plate recognition
• Traffic flow analysis
• Driver drowsiness detection

In medicine:

• X-ray and CT analysis
• Cell microscopy
• Surgical assistance

In everyday life:

• Photo filters
• QR code scanning
• Document recognition

How to implement machine vision in your business

Step 1: Define the task

The most common mistake is “we want machine vision, we don’t know what for”. Start with a specific problem: “we lose 5% of products due to defective packaging”, “we want to reduce the time of inspection from 30 to 5 minutes”, “we want to automatically count visitors”.

The clearer the task, the easier it is to choose a solution and evaluate its effectiveness.

Step 2: Choose between cloud and edge

Cloud (AWS, Google, Yandex): simple integration, pay-per-use, but data goes to third-party servers. Good for non-critical tasks, prototypes, low data volumes.

Edge (on the device): data is processed locally, no internet connection required, but you need a local computer (NVIDIA Jetson, Intel NUC). Good for industrial use, privacy-sensitive tasks, real-time.

For most business tasks, edge is preferred: data does not leave your network, response speed is 50–200 ms, no subscription costs.

Step 3: Choose hardware

For 90% of tasks, a regular 4K camera with autofocus is sufficient. For tasks requiring depth (measuring volume, working with three-dimensional objects) — a depth camera. For high-speed tasks (conveyor) — an industrial camera with a global shutter.

Step 4: Choose a model

There are two paths:

1. Pre-trained model + fine-tuning. Take a ready-made model (YOLO, Detectron2, EfficientDet) and train it on your data. Requires 100–500 examples for a simple task, 1000–5000 for complex ones.

2. Custom model. If the task is unique and there are no analogues — develop from scratch. This is expensive (months of work, a team of researchers) and only justified for critical tasks.

In our experience, 80% of tasks are solved by fine-tuning a pre-trained model. Custom development is needed only for very specific tasks (recognition of a specific defect type, work in non-standard conditions).

Step 5: Integrate

The model gives a result (object coordinates, classification, segmentation), but the result needs to be integrated into business processes. This is the most underestimated stage: the model can work with 95% accuracy, but if the result is not displayed to the operator or not connected to the warehouse system, the business value is zero.

What we do at Tavrida Robotics

We implement machine vision in our own robots and for clients:

• For robots: vision for navigation, object recognition, quality control (e.g., for the pruning robot — recognition of branches and cuts)
• For clients: turnkey solutions for production, agriculture, retail. From the formulation of the task to integration and support

Typical project: 2–4 weeks. Cost: 200,000 to 2,000,000 rubles depending on complexity. Payback: 10–18 months (typical for our experience, range 3–12 months in marketing materials).

Common mistakes

1. Start with technology, not with the task. “We need AI/ML” is not a task. “We need to reduce defective products by 50%” — this is a task.

2. Collect data before formulating the task. Without understanding what exactly we are looking for, data collection is useless.

3. Ignore integration. The model is 20% of the work. Integration into business processes — 80%.

4. Expect 100% accuracy. Even the best models give 95–98% accuracy on clearly defined tasks. The remaining 2–5% (and up to 10% on noisy or low-quality input) need to be handled by a human in the loop. Achieving more than 99% is generally not realistic without significant over-engineering.

5. Forget about support. Models degrade over time: lighting changes, products change, conditions change. Quarterly model retraining is the norm.

What to do next

If you have a task where computer vision could be useful — write to us via our contact form. We will conduct a free assessment: whether machine vision will help in your case, what accuracy is achievable, what the cost and timing will be.