How to Integrate Computer Vision Without Breaking the Budget

Computer vision is one of the most popular areas of artificial intelligence (AI) at the moment. Many people think that it’s limited to just facial recognition and object detection. However, this is only a small part of the computer vision iceberg. This technology already helps many industries achieve much more significant results in such tasks as biometric identification, diagnostics of various diseases, defect identification, traffic analysis, and customer behavior tracking.

The list of tasks that can be done with the help of computer vision is truly huge. However, the integration of this technology requires considerable funds, both for hardware and software. How can you save your budget in this process without losing quality? This is what we are talking about in this article. Read on to learn more.

What is computer vision and how does it work?

Computer vision is a field of AI that helps machines understand visual data in a kind of similar manner humans do. It works almost the same as large language models (LLMs), but instead of analyzing text, computer vision analyzes images, videos, and live feed from security cameras. Based on the input data and task on hand, it can detect objects, see patterns, and provide insights for decision-making. 

In simple terms, the process looks like this:

1. The system captures the necessary image from the data source (cameras, datasets).

2. The image is preprocessed. It includes things like noise reduction, brightness adjustment, and resizing.

3. The system identifies the necessary patterns, shapes, and textures and learns them automatically thanks to deep learning.

4. AI model classifies and labels the objects.

5. The system interprets results and performs actions based on them.

This is the basic description of the way computer vision works. With these steps, computer vision solutions can provide accurate results and optimize visual-based tasks.

Computer vision examples

Computer vision incorporates several major technologies that can perform a big number of tasks. The most prominent ones include:

Face recognition

Face recognition helps identify and verify people by analyzing facial features. This technology can be helpful, for example, in law enforcement during looking for criminals and in the tech industry for facial identification like Apple Face ID. However, face recognition is considered controversial by some people since it can give authorities means to track you 24/7. It also can be biased and inaccurate which can lead to inadequate treatment.

Person recognition

It can detect and track people across multiple cameras or images. For example, Amazon Rekognition is used in security and surveillance to identify people and Retail Analytics tracks the way customers move around the store for insights on their behavior.

Edge detection

With the help of edge detection, you can identify and outline the boundaries of objects in an image. This tech solution is useful for robotics to enable robots to “see” what’s in front of them. Other industries that can benefit from edge detection include healthcare (decoding MRI scans and X-rays) and automotive production (vehicles detecting road signs and markings).

Image restoration

Image restoration, as the name claims, can enhance and reconstruct images. It can help you remove noise or artifacts from the picture, restore the missing parts, or color the monochrome photos. More ways of image restoration also include bettering low-resolution images and removing objects from photos.

Optical Character Recognition

Optical Character Recognition (OCR for short) is a computer vision technology that converts printed, typed, or handwritten text into digital format. It can be used for various purposes. For example, you can extract text from images to search or translate it. Or, instead of storing all your documents on paper in one copy in an infinite number of boxes, you can digitalize all of them and store them on on-premise or cloud servers.

Object detection and classification

Since computer vision can identify the outlines of objects on an image or video, nothing stops it from seeing it as a whole and classifying it. The most prominent example of it is self-driving cars. They can detect pedestrians, vehicles, and obstacles to safely navigate the road.

Why does your business need computer vision?

Now that you know how computer vision works and what tech solutions are based on it, why exactly should you implement it into your business? How can computer vision bring your company to a new level? Here are the key point:

• Automation: Computer vision reduces manual work by automating routine and monotonous tasks like quality control, inventory tracking, and document processing.

• Better security and surveillance: Since computer vision can detect things on live camera feed, it can improve security with facial recognition and anomaly detection.

• Fun customer experience: Augmented reality (AR) powered by computer vision can create an interesting and engaging experience for customers in retail stores. Sephora did that with its AR mirror so customers can try on makeup virtually.

• Improved decision-making: Just like any AI, computer vision can help you analyze data (in this case, visual) and extract insights to build better strategies.

• Fraud detection: This technology can identify forged documents like IDs and signatures to strengthen your security even more.

Moreover, computer vision can provide more industry-specific benefits. They include:

• Healthcare: Computer vision has already proven itself to be useful in helping doctors detect tumors, fractures, and other abnormalities in X-rays and MRI scans.

• Manufacturing: You can apply live quality control to catch all product defects and misalignments in real time.

• Logistics: Logistic and transportation companies can get better inventory management, warehouse automation, and cargo inspection with computer vision solutions.

• Marketing: Image recognition can be used for targeted ads and personalized recommendations.

Challenges of computer vision

Even with all the benefits mentioned above, computer vision still comes with challenges that businesses and researchers need to be aware about. Here are some of the biggest ones:

• Data quality: This challenge is relevant to all branches of AI, including computer vision. These models require large datasets for training. And things like poor image quality, low resolution, or biased datasets will lead to inaccurate results and hallucinations.

• High costs: Training computer vision models and sustaining stable performance (especially for real-time work) require a lot of resources and these resources cost money. 

• Changing environment: Real-world environment is constantly changing. Lighting, weather, shadows, perspective—some systems can struggle with their tasks are the ever-changing conditions.

• Privacy and ethical concerns: Facial recognition in surveillance raises a lot of public concerns about privacy, consent, and bias. If your system is constantly watching people (especially without their knowledge), people are worried about 1) how your system evaluates them because of the possible bias and 2) what you are going to do with the data you receive.

• Security: Computer vision systems can be tricked by adversarial inputs. Small image modifications cause incorrect results, so the final results can’t be used for further research or surveillance.

Step-by-step computer vision integration guide

Now we are moving on to the action plan. We have prepared a short but valuable guide on how you can integrate computer vision into your software without spending a fortune on it.

Step 1: Define your use case

All software projects start with this step. Before diving into any initiative, you should clearly understand why you need this and what results you expect to achieve. For computer vision, the possible problems it can solve include examples like automating quality control or improving security measures. It’s better to start with a proof of concept (PoC) and open-source tools before investing in more sophisticated AI solutions.

Step 2: Choose an approach

Here, you can choose from two options:

• Using pre-trained models (faster and cheaper)

• Training a custom model (more flexible but require more resources)

You can definitely pay attention to services like Google Cloud Vision, AWS Rekognition, and OpenCV. Depending on your task, you can choose more industry-specific tools.

Step 3: Prepare your data

Your model needs high-quality images/videos for training. There’s no cutting corners here because the accuracy of outputs will depend on the initial materials. You need to gather real-world images related to your task, then label it and remove as many issues as possible (augment resolution, rotate images, cut videos if necessary). In the beginning, you can use public datasets like ImageNet or Google Open Images to reduce costs.

Step 4: Choose the platform

Decide where you’ll develop and deploy your model. Some budget-friendly tools include Google Colab (with free GPU access), NVIDIA Jetson Nano (affordable edge computing), and TensorFlow Lite (for running models on mobile devices).

Step 5: Train your model (if needed)

If you decide to use a tailored model, you need to train it. Deep learning frameworks like TensorFlow and PyTorch can help you with that. You can also use transfer learning (fine-tune pre-trained models) instead of training from scratch to save time and costs.

Step 6: Deploy into your system

Decide where to run your model. On-premise servers are good for real-time processing (like factory floor cameras), but their installation and maintenance will cost more. Cloud-based storage is scalable but may have latency issues (also can cause security concerts). The choice will depend on your goals, needs, and budgets.

Step 7: Optimize and maintain the system

There are several metrics that you need to track to see if your computer vision model works as intended:

• Accuracy—How well does the model detect/classify objects?

• Latency—Is it fast enough for real-time use?

• False positives/negatives—Minimize errors to improve reliability.

If these metrics are fine, then your model is good to go. Please note that you need to check them continuously to catch any mistakes/biases and update your model in time.

To sum up

Computer vision is an innovative technology that can help a lot of businesses. It can help your business too. Whether you want to get a better quality control on your production lines or to digitalize your invoices, computer vision is your AI friend that will come in handy.

If you want to implement computer vision into your business, contact us! Yellow is an AI software development company that is experienced in building top-tier AI solutions for various businesses.