If you are passionate about AI and love hands-on work with visible results, becoming a computer vision engineer is a great career option.
In this guide, we are going to talk about:
- What a computer vision engineer is;
- The educational requirements for a vision engineer job;
- The skills you will need to be a computer vision engineer;
- The tools of the trade, and more.
Let's get started!
What is Computer Vision
Computer vision, a branch of AI, allows computers to see and understand the real world. Applications like facial recognition, flaw detection, autonomous vehicles, and medical imaging use computer vision techniques to improve efficiency and accuracy.
What is a Computer Vision Engineer?
A computer vision engineer is a developer who specializes in creating software solutions that can extract visual information and insights from images and videos. Computer vision is widely used in industries like robotics, automotive, finance, manufacturing, healthcare, and agriculture.
For instance, take a conveyor belt carrying finished products in the manufacturing industry. Computer vision can be used to inspect the products to look for defects such as cracks, scratches, or any minute deformation that might be missed by human inspection to ensure quality and reduce waste.
Computer vision models can perform tasks that humans typically do but with greater efficiency.
Educational Background and Prerequisites
The traditional route to becoming a computer vision engineer begins with choosing a relevant college major like computer science or information technology. You can also use online courses to learn about computer vision if you already have programming knowledge or a technical background.
In any case, whether you attend a course or college, you will learn about key topics like machine learning, data structures, deep learning, and image processing techniques.
Having a strong mathematical foundation, particularly in linear algebra, calculus, probability, and statistics, would help you brainstorm solutions to real-world problems.
In your studies, you will learn Python and various visual processing libraries, such as:
- OpenCV: Used for image processing, it provides tools to manipulate images and videos, like detecting objects, faces, and features.
- TensorFlow: A popular framework for machine learning and deep learning, particularly useful for building and training neural networks.
- PyTorch: Another deep learning framework, favored for its flexibility and ease of use when experimenting with neural network models.
- Supervision: Provides reusable computer vision tools, which are crucial for developing computer vision solutions.
- Transformer: A library that gives developers access to pre-trained models for tasks across various domains like computer vision, natural language processing, and audio.
In some cases, you might also need to learn other languages like C and C++, especially when working on performance-critical parts of a project, such as optimizing code for real-time processing or interfacing with hardware (e.g., cameras, sensors). These languages are often used in embedded systems and environments where speed and efficiency are vital.
That said, many computer vision engineers take a different path. So, even if you didn’t study computer science or IT, don’t worry - you can still break into the field. If you're new to coding, it’s completely possible to learn the essential programming languages and tools along the way. For beginners, check out this guide on computer vision Python packages to start and get your hands dirty with real-world projects.
Core Skills Required for a Computer Vision Engineer
Coding and mathematical skills are the building blocks for computer vision. Along with that, a strong understanding of core computer vision concepts like image processing, object segmentation, and machine learning is key to solving real-time and practical applications.
Image Processing
Image processing is a fundamental concept used in computer vision applications to manipulate, enhance, and analyze digital images. It is particularly useful in computer vision because it helps refine digital images for advanced analysis. Most image processing techniques make pixel-level changes to images, like adjusting their intensity to brighten or darken them.
Here are some more examples of image processing techniques:
- Filtering involves removing noise and unwanted elements to enhance an image, with Gaussian filters being widely used for this purpose.
- Edge detection creates boundaries between different objects by identifying discontinuities that correspond to various objects or regions through intensity variations between neighboring pixels.
- Object segmentation isolates distinct objects in an image using parameters like color, shape, texture, and depth.
- Thresholding is primarily applied to grayscale images, converting them into binary images based on a predefined threshold value. Pixels below this threshold turn black (0), while those above become white (1).
Machine Learning and Deep Learning
Machine learning and deep learning models in computer vision make it possible for computers to perform tasks usually done by humans, such as scene classification and medical diagnostics. These models often involve convolutional neural networks (CNNs). CNNs consist of multiple layers, each extracting different information from images, that can help with computer vision tasks like object detection, segmentation, and classification.
An Example of Object Detection (Source)
Another commonly used model is Generative Adversarial Networks (GANs). These networks feature two components: the generator, which creates new samples, and the discriminator, which evaluates their authenticity. GANs are primarily used for image generation and data augmentation.
Algorithms and Optimization Techniques
When it comes to computer vision, it’s essential to know the ins and outs of algorithms, optimization techniques, and performance tuning for various applications. These factors directly impact how accurate and fast detection is, which is important for real-time innovations like autonomous driving and surveillance systems. These applications have very little room for latency; data must be captured, processed, and acted upon within microseconds. Optimization techniques such as pruning, model distillation, and quantization can help you meet time constraints without sacrificing accuracy.
It’s also important to know what works best for different scenarios. For example, both MobileNet and VGG16 are convolutional neural network (CNN) models that can be used for image classification and object detection.
While they can produce similar results, MobileNet is 32 times smaller and ten times faster than VGG16. MobileNet is ideal for mobile and embedded devices with limited computational power, whereas VGG16 is better suited for high-accuracy tasks without computational constraints. Knowing these technical details is key because choosing the right model can make a huge difference in overall performance and efficiency.
Tools and Frameworks You Should Master
If you are a beginner, starting with the Roboflow notebooks in our GitHub repository is a great choice. They provide step-by-step guides for various computer vision tasks, with detailed explanations, ready-to-use Python templates, and video tutorials to help you along the way.
It’s also a good idea to be familiar with frameworks like OpenCV. OpenCV serves as a foundational framework for computer vision models. It is open-source and accessible for contributions. Alongside OpenCV, mastering TensorFlow, PyTorch, and Keras is beneficial. They are widely used for tasks like object detection and natural language processing. For deploying models, cloud services like AWS, Google Cloud AI, and Microsoft Azure provide the necessary infrastructure and resources.
Computer Vision Certifications and Courses
Structured online computer vision courses from platforms like Roboflow, Udacity, edX, or Coursera are great resources for learning new skills. The Nanodegree Computer Vision Program by Sebastian Thrun on Udacity is particularly valuable for beginners, covering essentials like CNNs, Image Classification, and Cloud Computing. Updated in October 2024, it includes the latest advancements. Similarly, Andrew Ng’s Deep Learning Specialization course provides extensive knowledge on neural networks and optimization, though it requires a solid foundation.
If you are new to coding, you can try online courses like “Introduction to Computer Vision and Image Processing by IBM” on Coursera. It is a beginner-friendly course with no or little programming skills needed. It covers image processing, machine learning, CNNs, object detection, and a real-time project. For advanced learners, Stanford’s Deep Learning for Computer Vision course offers a deep dive into recent industry developments.
For more sophisticated hands-on learning, explore this GitHub repository for a list of courses, and refer to Roboflow Learn for a dedicated learning platform. On Roboflow Learn, you can find a range of resources, including blog posts covering topics from the basics of computer vision to practical applications like controlling OBS Studio. The platform also features many videos and tutorials on training computer vision models, along with a dedicated YouTube channel for in-depth content.
To strengthen and enhance your skills, you can also consider obtaining certifications such as Google’s TensorFlow Developer Certificate or Microsoft’s Azure AI Engineer Associate Certificate.
Building a Strong Portfolio
Once you have mastered the basics, you can start working on open-source or personal projects to build your portfolio. There are various great options to explore. You can sign up for Kaggle, a data science competition platform, to take part in various competitions and refine your skills. Or, you can train and test your custom model on no-code training platforms to upskill your problem-solving and creative thinking capabilities.
Contributing to Supervision is another interesting way to help build your credibility. As we saw earlier, Supervision is a Python package that offers a suite of reusable tools to streamline development. It enables users to use computer vision for various tasks, including object detection, segmentation, annotation, and visualization. For example, Supervision libraries can help you detect small objects in large areas.
Similarly, contributing to GitHub repositories and OpenCV can give you valuable experience. After working on personal and open-source projects, the Vision AI community encourages applying for internships in computer vision roles. Focus on industries like robotics, autonomous vehicles, or healthcare, where the demand for computer vision engineers is high.
Networking and Staying Updated
Porter Gale, a well-known networker and entrepreneur, has emphasized the value of connections by saying, “Your network is your net worth.”
Networking is a key skill, not just in computer vision but in any profession. By attending events like the Conference on Computer Vision and Pattern Recognition (CVPR) or the European Conference on Computer Vision (ECCV), you can build connections and keep you updated on industry trends. You can join also online communities on platforms like LinkedIn, Stack Overflow, Reddit, and GitHub for networking and collaboration. Being active in online communities and sharing insights can help you build your professional network.
Computer Vision Career Path and Job Opportunities
As an entry-level computer vision engineer, you can expect to primarily focus on data labeling, basic algorithms, and collaborating closely with senior engineers. Your responsibilities will also include staying updated on industry trends, assisting team members with using and training computer vision models, and maintaining models thorough documentation.
You can then progress onto a senior role, which will involve more complex modeling, leading projects, and working more closely with organizational data pipelines for use in vision applications.
According to Indeed, the average base salary for a computer vision engineer in the USA is $122,948 per year, with a range of $72,761 to $207,752. In England, the average salary is $94,687 per year. As mentioned in Geeksforgeeks, in terms of experience, entry-level engineers (0-2 years) can get $8000 to $12000 per month. Mid-level engineers (3-5 years) can earn $13000 to $18000 monthly. Senior-level engineers (6+ years) can get $19000+ per month. However, the salary range can depend on factors like location, company, and technical skills.
Conclusion
Computer vision is a rapidly evolving field that requires continuous learning and hands-on experience. To excel in this field, stay updated on the latest advancements, experiment with different models and datasets, and develop strong problem-solving skills. Engage with the computer vision community by attending workshops and conferences.
Building a strong portfolio, networking with industry professionals, seeking mentorship, and pursuing relevant certifications can help nurture your career. Along the way, contributing to open-source projects and staying active on platforms like GitHub can showcase your expertise in this field. Build practical skills, create real-time solutions for real-world problems, and start your journey toward becoming a computer vision engineer!
Cite this Post
Use the following entry to cite this post in your research:
Abirami Vina. (Oct 30, 2024). How to Become a Computer Vision Engineer. Roboflow Blog: https://blog.roboflow.com/how-to-become-a-computer-vision-engineer/
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