How to Become a Computer Vision Engineer in 2024?

Becoming a Computer Vision Engineer - Learn what a computer vision engineer job entails and the key skills required to become one.

How to Become a Computer Vision Engineer in 2024?
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Computer Vision Engineer 

You are on a long drive, and the road stretches far in front of you. It’s getting dusky, and there isn’t much traffic on the highway. With each passing mile, the signboards blur into one another as the speedometer keeps constant. You have been on the road since morning, and you are feeling jagged and jaded. The lane markings painted on the road have become one thick fluid line, sending you in a dream-like state. You are falling asleep, and you know the next coffee stop isn’t anywhere near. 

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A scenario like this is scary for everyone on the road. These situations occur quite often and are the reason for many road accidents on interstate highways. Similar cases are avoidable with the advent of self-driving or autonomous vehicles—an example of computer vision in use, and all thanks to computer vision engineers.

Autonomous Cars: A Computer Vision Application

Autonomous Cars: A Computer Vision Application

Computer vision is the technology that identifies objects in the real world and makes sense of them in real-life applications. It is used widely in medicine, military and defence and manufacturing etc. Computer vision holds a promising future ahead, so let’s reap the benefits together as a prospective computer vision engineer and a grateful user.


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Who is a Computer Vision Engineer?

A Computer vision engineer works at the crossroads of machine learning that simulates human-like vision. He is responsible for developing and automating computer vision models that make our work and life easier. Computer Vision engineers are accountable for developing and testing Computer Vision solutions for real-life problems and applications. They also interact with the engineering team and the client to innovate new products and features and incorporate real-time feedback. Alongside all this, they participate in prototype building and testing for new technologies and ideas which down the line, might become full-fledged products that the company can offer.

Computer Vision Engineer

As the computer vision domain keeps growing and more startups are getting onboard with computer vision business and analytics, the distinction between a computer vision scientist and a computer vision engineer is getting thinner. Computer vision scientists get to work at research labs spending time with cutting edge deep learning algorithms and state of the art architectures.  

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Computer vision engineers at small startups have to juggle both these roles together at times. They would need to scour the internet for new research papers and upcoming techniques to keep at the level of the research and apply the said techniques to the application.  It is essential to read through the computer vision engineer job description properly to understand what will entail in the course of the work in the company. 

Computer Vision Engineer Job Outlook 2024

Computer Vision Engineer Job Outlook 2023

Computer vision is proliferating, with the demand for Computer vision engineers soaring higher than ever. There are at present over 60,000 job postings alone in the US, and this number is steadily increasing year on year. Top tech companies like Apple, Amazon, Facebook, Google, and Rockstar Games are looking to hire professionals with computer vision skills. These facts clearly show that computer vision engineer jobs hold great potential in 2021 and beyond. 

Considerable research and novel innovation are happening in computer vision using state of the art machine learning techniques like Deep Learning, CNN, Tensorflow, Pytorch, etc. Computer vision will grow commensurately as fields like machine learning and data science see significant advancements. The use-case of computer vision technologies is transitioning to the public domain. Computer Vision applications are increasing and will see massive adoptions down the years in the future. It is a favourable time to master some state-of-the-art computer vision skills and flourish with the market. 

Computer Vision Engineer - Roles and Responsibilities

Veoneer Job Description from Linkedin

Veoneer Job Description from Linkedin

Uber Computer Vision Engineer Job Description from Linkedin

Uber Computer Vision Engineer Job Description from Linkedin

Computer Vision Engineer job role includes developing computer vision models, retraining them, creating reliable quality datasets, libraries and scouring research papers to implement novel solutions specifically tuned to the product. 

Often, it is observed that job postings and job roles for computer vision are simply tagged as software engineers by startups and mid-level corporations. A good practice is to go through the complete job description available and the expectation provided by the company. The reason for companies to use general titles like software engineer a software developer may be attributed to the fact that alongside computer vision duties, an engineer would also need to partake in software development and engineering tasks.

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The job roles and responsibilities in computer vision are different for each position since it is organisation dependent and experience-specific. Some of the typical day-to-day tasks a computer vision engineer performs include -

  • Computer Vision model development

  • Training data creation 

  • Automating computer vision tasks like mask detection, animal and cattle tracking in farmlands and parking vacancy detection. 

  • Review code and collaborate with domain experts from machine learning and data science.

  • Prototyping algorithms and testing to articulate/quantify results. 

  • Image restoration

  • Scene construction

  • Motion analysis

  • Object recognition

  • Read latest journals and research papers

It is worth mentioning that top tech companies usually have a division of work and role-specific tasks. A computer vision engineer will be redesigning and providing support for already implemented computer vision products with some development in top tech companies. While in small startups, a computer vision engineer is required to wear many hats and test already implemented solutions and build new ones. Startups generally provide overall growth and help broaden skills in all areas of computer vision. It is essential to read the job description thoroughly to understand what the job role entails. 

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Educational Background Needed to become a Computer Vision Engineer 

Computer vision engineers need to have a full-time degree in computer science or engineering with a specialization in computer vision or advanced machine learning concepts. The degree can be Masters, Bachelors or PhD. They should possess object-oriented programming skills.

  • An MS, BE or PhD degree in Computer Science Engineering or Electrical Engineering with courses around computer vision. 

  • Certifications and courses that add to computer vision expertise are good to have but not a must-have for computer vision engineer jobs.

The educational qualifications discussed above are neither exhaustive nor strictly mandatory. There are many exceptions in the industry who do not hail from a STEM background but have made successful career transitions as computer vision engineers. The most critical background required is the willingness to learn and work hard. Everything else is a bonus.

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Skills Required for Becoming a Computer Vision Engineer

While many engineers can’t change their background, it is always possible to hone new skills. You can upskill in any new tools and technologies at any point in time of your career. It could be in college, in a new role or after a 10-year career in any job role. Upskilling helps you make a tangible difference and propel you forward in any job role.  Here’s a checklist of key computer vision skills to have -

  • One should display object-oriented programming skills and a basic understanding of software development concepts and lifecycle since the work revolves around software engineering and development. One needs to hone robust software testing and debugging skills as well.

  • You need to be well versed in languages and libraries like Python, C++, Matlab, R, SQL server, OpenCV, etc.

  • Some history in research paper publications in conferences like ICCV and ECCV is appreciated and is seen as a standout factor.  

  • You need to have a rudimentary grasp of linear algebra principles such as matrix factorisation, principal component analysis, dimensional reduction, linear transformation, matrix multiplication, etc.

Let us take a comprehensive look at the skillset that one needs to become a computer vision engineer -

Computer vision is a subset of machine learning that extensively uses deep learning models like CNN, RNN, ANN, just to name a few. You will need to have the know-how of machine learning algorithms to classify images or detect objects.

Python, MATLAB are the most common programming languages used to build computer vision projects. Each language provides its own set of features and frustrations.

    • Python offers numerical manipulation libraries like NumPy and SciPy and dedicated integration with the computer vision library called OpenCV. Python also provides many machine learning libraries. 
    • MATLAB is convenient for quick prototyping and implements new ideas. Adaptive to the academic and research environment, it offers utilities like matrix multiplication, plotting functions, data visualisation etc.  It is not suitable software for a production environment with specific memory and performance problems. 
    • C++ is close to the hardware language that offers high performance with library support for machine learning and OpenCV like LibSVM, SVMlight, Torch, etc. The prototyping speed and development time take a toll due to the complicated syntax and writability of code. 
    • Cython

Cython is used when python code needs to be converted to C code. It offers high performance and is close to metal implementations like C and C++. It is a midway point between the ease of python code and the super-fast runtime in  C 

  • TensorFlow 

Tensorflow is an open-source library for machine learning to develop and train neural networks for deep learning and many machine learning models that use heavy numerical computation. It was developed by the Google Brain team back in 2015

  • YOLO

You Only Look Once, or YOLO is a real-time object detection algorithm. It uses Convolutional Neural Networks as its core to detect objects in real-time.

  • OpenCV

OpenCV is an open-source library for image processing and computer vision tasks.

  • MATLAB

MATLAB is a programming language that provides an environment for image manipulation and large-scale numerical analysis and plots. It also provides a dedicated computer vision support called Computer Vision ToolBox for implementing and testing CV solutions. 

  • Keras

Keras is an open-source library for python that is used to implement deep learning models. It acts as a wrapper over Theono and Tensorflow libraries. 

  • CUDA

CUDA is an API developed by Nvidia for parallel computing and graphical processing that uses GPU to boost performance.

  • DeepFace

DeepFace is a deep learning platform system created by Facebook in 2014. It excels at identifying faces in images. 

  • PyTorch

PyTorch is an open-source library in python offering easy-to-use methods for natural language processing and image processing. 

Some other libraries used widely in computer vision are OpenGL, PyTorch, Dlib, PyTesseract, Scikit-image, Matplotlib, IPSDK, Mahotas, FastAI etc. It is good to have the know-how of at least two or more of the libraries mentioned above.

Background with Foundational mathematics like linear algebra, 3d geometry and pattern recognition, basic convex optimisations, gradients in calculus, Bayesian Probability is helpful and good to have.

Computer Vision Techniques to Master

Following are some important computer vision techniques:

  • Image segmentation

It is the process of breaking the image into segments for easier processing and representation. Each component is then manipulated individually with attention to different characteristics. 

    • Semantic segmentation

Semantic segmentation identifies objects in an image and labels the object into classes like a dog, human, burger etc. Also, in a picture of 5 dogs, all the dogs are segmented as one class, i.e. dog.

There are two ways to go about semantic segmentation. One is the route of classic and traditional algorithms, while the other dives into deep learning.

Fully Convolutional Network, U-net, Tiramisu model, Hybrid CNN-CRF models, Multi-scale models are examples of Deep Learning algorithms

Grey level segmentation and conditional random fields are examples of traditional algorithms for Image Segmentation.

    • Instance Segmentation 

Unlike semantic segmentation, objects in the image that are similar and belong to the same class are also identified as distinct instances. Usually more intensive as each instance is treated individually, and each pixel in the image is labelled with class. It’s an example of dense prediction.

For example, in an image of 5 cats, each cat would be segmented as a unique object.

Some common examples of image segmentation are:

      • Autonomous Driving Cars

      • Medical Image Segmentation

      • Satellite Image Processing

  • Object Localisation 

Object localisation is the process of detecting the single most prominent instance of an object in an image.

  • Object Detection

Object detection recognises objects in an image with the use of bounding boxes. It also measures the scale of the object and object location in the picture. Unlike object localisation, Object detection is not restricted to finding just one single instance of an object in the image but instead all the object instances present in the image.

  • Object Tracking

Object tracking is the process of following moving objects in a scene or video used widely in surveillance, in CGI movies to track actors and in self-driving cars. It uses two approaches to detect and track the relevant object/objects. The first method is the generative approach which searches for regions in the image most similar to the tracked object without any attention to the background. In comparison, the second method, known as the discriminative model, finds differences between the object and its background. 

  • Image Classification

Classification means labelling images or subjects in the image with a class that relates to the meaning. Following are some of the standard image classification algorithms you must know -

  • Parallelepiped classification 

  • Minimum distance classification

  • Mahalanobis classification

  • Maximum likelihood

Some common examples of classification are:

Image recognition, object detection, object tracking.

  • Face Recognition

Face recognition is a non-trivial computer vision problem used to recognise faces in an image and tag the faces accordingly. It uses neural networks and deep learning models like CNN, FaceNet etc. 

Firstly, the face is detected and bordered with bounding boxes. Features from the faces are extracted and normalised for comparison. These features are then fed to the model to label the face with a name/title. 

  • Optical Character Recognition 

OCR is used for converting printed physical documents, texts, bills to digitised text, which is for many other applications. It is a crossover of pattern recognition and computer vision. A popular open-source OCR engine developed by HP and Google and written in C++ is Tesseract. To use Tesseract-OCR in python, one must call it from Pytesseract. 

  • Image Processing 

One needs to have a ground understanding of simple image processing techniques like histogram equalisation, median filtering, RGB manipulation, image denoising and image restoration. Image regeneration or restoration is a prevalent technique of taking a degraded noisy image and generating a clean image out of it. The input image can be noisy, blurred, pixelated or tattered with old age. Image restoration uses the concept of Prior to fill in the gaps in the image and tries to rebuild the image in steps. In each iteration the image is refined, finally outputting the restored image. 

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How to Become a Computer Vision Engineer?

The road to becoming a computer vision engineer is challenging but equally rewarding. The benefits of working in a blooming industry are many. Not the least of which is great compensation, working with experts from other fields, building models from scratch and adding value to society. 

Implementing projects from scratch is a sure-shot way to learn the underlying concepts and principles related to computer vision.  The algorithm for detecting objects in an image is completely different from the technology and algorithm for 3D modelling. Working on diverse computer vision projects exposes you to novel emerging tools, technologies, and algorithms in computer vision. Here are few computer vision project ideas that you can start working on to begin your learning journey -

1. Computer Vision Project for Neural Style Transfer with GANs

Neural Style Transfer takes the style of one image and uses it to recreate another image in the same type. It is accomplished by tuning the content statistic of the final/output image to the style statistic of the style image and content of the content image. Content image is the original image that is recreated in a different style. Style image is the source image that provides style information. Two common datasets used to implement this computer vision project idea are COCO and ImageNet.

2. Face Recognition Project using FaceNet

Face Recognition Project using FaceNet

The project aims to identify and classify a person’s face from images and videos. The dataset used is a video from the famous 90s sitcom Friends. Snaps from the footage are used for training the model and categorising the cast’ faces. One will learn to implement TensorFlow, Keras, Convolution Neural Networks for training and the Haar Cascade algorithm for face detection. 

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3. Object Detection Project with DETR

Object Detection Project with DETR

Detection Transformers are models that can detect objects of interest from images and label the subject appropriately. Innovated by Facebook and open-sourced later, DETR follows the encoder-decoder architecture. You can use MS-COCO and Open Images dataset to work on some interesting object detection projects.

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4. Road Lane Detection Project

Road Lane Detection Project

Road Lane Detection is a relatively advanced project in computer vision that integrates many technologies. Autonomous car companies use it to detect the curvature of the road as a steering mechanism.  Self-driving cars have sensors and cameras around them that take information from the outside world continuously. The camera captures pictures of the road, traffic signal, road signs and nearby vehicles. While the Light detection and ranging ( LIDAR ) sensors throw flashes of light onto the surrounding environment to detect the lane markings, road edges and distances from various obstacles. 

5. Machine Translation Human Pose and Intention Classification

The project tries to identify postures and gestures of pedestrians crossing a street or in the process of doing so. It calculates the skeletal structure of the human to detect whether the subject is in motion, standing or at rest. Keyframes images are extracted from the video. A sequence to sequence model processes keyframe information from the video as a sequence of images. A 2d bounding box encloses the pedestrians and tracks their moves. Skeletal structure and bounding box information are used to train the DenseNet model.

To explore some more interesting computer vision project ideas, check 15 Projects for Beginners in Computer Vision.

Recommended Reading -

Whether you want to learn computer vision or learn machine learning or anything else, for that matter of fact, a starting point is always required. Of course, the best way to get your hands dirty on a concept is to learn by doing some hands-on projects, but some best books on computer vision can teach you the underlying principles, applications, and the pros and cons of computer vision. Regardless of your expertise in computer vision, books are always good to read.

If time is not a constraint, one should refer to books for basic concepts and ideologies. The fundamentals in computer vision have not changed much over the years, and books provide detailed explanations and relevant history. Three books to get you started:

    • Computer Vision: Algorithm and Application by Richard Szeliski

    • Computer Vision: model, learning and inference by Simon Prince

    • Computer Vision: A modern approach by David Forsyth and Jean Ponce

All the algorithms that find use in computer vision are built on mathematical prerequisites like statistics, probability, linear algebra and calculus. Knowing the math will help you understand why you should choose one over the other and how the performance of a model affects it. This makes learning more fun and interesting.

Reading research papers is an excellent way to keep up-to-date with the current tech advancements in the field of computer vision. Research papers contain lots of technical jargon condensed to the point. Hence, getting in the habit of reading these will help a concrete understanding of the subject. Google Scholar is a great source for these as it curates many research papers on various topics that one can browse.

Get a good grasp and know-how of popular machine learning and deep learning models like Convoluted Neural Network, Recurrent Neural Network, Support Vector Machines, Random Forest Classifiers, Generative Adversarial Network, Autoencoders etc. by working on a variety of datasets and projects.

Computer Vision Engineer Salary - How Much do they Earn?

Computer Vision Engineer Salary

Source : PayScale.com 

Like any other job role, the average salary for a computer vision engineer depends on the company, level of experience, job role, etc. On average, in the US, computer vision engineers make 117k USD per annum.  While the entry-level computer vision engineer salary falls around 94k per annum, the senior-level engineers can earn upwards of 140k every year. The average salary of a computer vision engineer in India is INR 600,000 to 800,000 per annum. It can go as high as 20 lakhs per annum for a professional or senior level job role.

The average salary of a computer vision engineer in India

Source: Glassdoor.com 

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Computer Vision Engineer Interview Questions 

We’ve collated some commonly asked computer vision interview questions and answers that will help you boost your confidence and preparation for your next computer vision job interview.

1. What are Sampling and Quantization?

Sampling and quantisations are processes that convert analogue images into digital copies. Sampling is the digitisation of coordinates of analogue images, while quantisation is the process of digitising the amplitude and intensity of an analogue image.

2. What is Transfer Learning?

Transfer learning ensures that the model learnings and accuracy stay constant even if the use case scenario and input data are slightly changed or varied. It adds a layer to the end of the model and saves time by training only the new layer instead of the whole model again. Hence, it transfers learning from the older model to the new model with an extra layer to accommodate the physical location or application changes. 

3. What is the difference between YOLOv3 and SSD?

You Only Look Once (YOLO) and Single-Shot Detection (SSD) are object detection techniques.

  • SSD has only one fully connected neural layer in its architecture, while YOLO contains two fully connected layers.

  • SSD is faster than YOLO as it has only a single convolution layer.

  • SSD also has a 1x1 filter that helps in detecting small objects and edges in the image.

4. What is 1x1convolution?

It is a feature pooling or feature reduction technique used to reduce the large sets of features in an image into a small group that can be processed efficiently. A 1x1 convolution outputs only the most significant feature maps in the image and drops the less critical features that dont add much information about the picture. An extensive feature set also implies a longer training time which is not desirable. 

5. How does one detect horizontal and vertical edges in images?

The vertical and horizontal edges can be detected by matrix multiplication. There are two matrices of importance here. 

Vertical Edge Detection Matrix - A matrix with 0 values in the middle column and non zero- preferably one- values in the other columns

Horizontal Edge Detection Matrix  - A matrix with 0 values in the middle rows while the out rows have non-zero values. 

Multiplication Of the image matrix with the vertical edge detection matrix will output the vertical edges in the image and vice versa for the horizontal edges.  

6. How is max-pooling different from average pooling?

Both max pooling and average pooling are techniques to reduce the image feature and dimension.  While max-pooling chooses the largest value in the feature matrix to retain, average pooling takes the mean of all the values in the feature matrix. 

Explore 25 Interview Questions and Answers in Computer Vision 

A word of advice to people who are transitioning from a more traditional software engineer job into a computer vision role is to keep at it and complete as many different projects in computer vision. It may look hard at first but doing projects from scratch as an approach is rock-solid. If you do feel like you need personal guidance, don't look any further than ProjectPro for solved end-to-end data science and machine learning projects curated to meet your learning goals with personalised learning paths.

 

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