Should you switch to learning Computer Vision (CV)?

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Tags: ai, computer vision, job, opencv

Are you a seasoned developer looking for a new specialization to learn? Have you considered 3D games, AR/VR, and IoT, but ultimately been drawn to Computer Vision?

Let’s explore whether investing in this field is worthwhile, and what challenges and opportunities await you.

Table of Contents

Computer Vision: Promising potential, but full of challenges

Computer Vision is a branch of artificial intelligence (AI) that enables computers to “see” and understand images and videos like humans. The applications of Computer Vision are incredibly diverse, ranging from face detection and object classification to self-driving cars and medical diagnosis.

Are you passionate about automation and have started exploring OpenCV and Keras? You’ll undoubtedly be captivated by Computer Vision’s immense potential to automate processes and address complex problems.

For example, you can use Computer Vision to automate product quality checks in factories, analyze traffic conditions to optimize traffic flow, or build more efficient security surveillance systems.

However, mastering Computer Vision is no walk in the park. It requires in-depth knowledge of Machine Learning, Deep Learning, and advanced programming skills.

Challenges you might encounter

Investment costs: The cost of equipment and dedicated graphics cards for training DNNs can be quite high, especially compared to developing iOS applications. You need to invest in powerful CPUs/GPUs and ample memory to meet the complex computational demands during training.

Complex processes: Preparing data, training models, and optimizing models are time-consuming tasks that require meticulous attention. You need to gather a large amount of high-quality data, then pre-process it, label it, and select the appropriate network architecture for model training. Model optimization also requires specialized knowledge and experience to improve accuracy, processing speed, and mitigate overfitting.

Lack of prominent individuals: Although Computer Vision is flourishing, there aren’t many prominent individuals with their own DNN models in Vietnam and Asia. This makes some people apprehensive about competitiveness in this field. However, this isn’t a major concern as you can reuse and fine-tune existing models to meet your needs.

Contrasting perspectives

“There’s no need to invent new models, just understand how they work.” – With the development of foundation models like grounding dino and sam2, you can reuse and fine-tune existing models instead of creating entirely new ones.

“OpenCV is easy to customize, you don’t need deep knowledge of ML and DNN.” – While OpenCV is a powerful library, to effectively customize it and address complex issues, you still need to grasp the principles of ML and DNN.

“Pay attention to camera setup, image capture, lighting…” – Choosing appropriate equipment, setting up cameras and lighting properly will enhance model efficiency, reduce errors, and create more sustainable applications.

Example: If you want to build a security surveillance system using Computer Vision, you need to select high-resolution cameras with wide field of view, capable of operating in low-light conditions, and set up appropriate lighting to ensure clear and accurate images.

Applications of Computer Vision: Unlocking a new world for technology

Field	Applications	Examples
Security & Surveillance	– Face recognition, user authentication – Detection of unusual behavior, danger alerts – Traffic monitoring, parking lot management	– Security control systems at airports and buildings – Smart surveillance cameras, fire detection – Automated traffic flow control systems
Healthcare	– Medical image diagnosis, disease analysis – Surgical assistance, disease treatment – Health monitoring, rehabilitation	– X-ray, MRI, CT image analysis – Robotic surgery, cancer treatment – Heart rate, blood pressure, physical activity monitoring
Manufacturing & Industry	– Product quality inspection – Production process automation – Predictive maintenance, occupational safety	– Product defect detection on production lines – Automated robot operation, goods handling – Early detection of potential failures, alerts
E-commerce	– Product search through images – Recommending products based on needs – Cashless payment	– Online shopping applications, product search – Personalized product recommendation systems – Face and fingerprint payment
Transportation & Automation	– Self-driving cars, driver assistance systems – Traffic analysis, traffic flow control – Road monitoring, traffic safety	– Autonomous vehicles, cruise control systems – Intelligent traffic control applications – Speed monitoring systems, fines
Entertainment & Media	– Image effects, video processing – Video games, virtual reality – Content creation, video analysis	– Special effects in films – VR/AR games, interactive games – Video analysis, detection of violating content

Note: These are some examples of Computer Vision applications; there are many other potential applications across various fields.

Comparing the Advantages and Disadvantages of 3 Learning Fields for Developers

Field	Advantages	Disadvantages
3D Games	– Large market, high demand – Opportunities for creative development – Ability to create engaging entertainment products	– High competition, requiring advanced skills – Difficulty in finding suitable jobs – High investment costs for hardware and software
AR/VR	– Emerging field with great development potential – Diverse applications across various industries – Opportunities to create unique experiences	– Technology is still immature and needs time to mature – Low market demand, difficulty in finding jobs – High development costs
Computer Vision	– Real-world applications, solving real-world problems – Ability to automate many processes, improve efficiency – Growing market, many job opportunities	– Requires in-depth knowledge of Machine Learning, Deep Learning – High investment costs for equipment and software – Work can be time-consuming and demanding

Comparing the Advantages and Disadvantages of Computer Vision vs. iOS App Development

Feature	Computer Vision	iOS App Development
Market demand	Rapidly growing, many potential applications	Large market, many popular applications
Competition	High competition, requires in-depth knowledge	High competition, requires iOS programming skills
Investment costs	High: powerful equipment, specialized software	Low: usually uses personal equipment, free software
Development time	Long: data preparation, model training, optimization	Short: relatively fast app development
Necessary skills	Machine Learning, Deep Learning, programming	Swift/Objective-C, iOS app development, UI design
Complexity	High: complex techniques, requires deep understanding	Moderate: complexity depends on app type
Career opportunities	Many opportunities: growing industry, high demand	Many opportunities: large market, stable demand
Earning potential	High: jobs can pay high salaries	Moderate: average salary, potential for additional income

Note: This comparison table is for reference only, as actual situations may vary depending on specific circumstances and fields.

Recommendations

Try using cloud platforms like Colab and Kaggle to test and fine-tune models.
Don’t worry too much about creating a standalone DNN model; focus on understanding how existing models work and how to effectively apply them to real-world applications.
Try your hand at common applications before delving into more complex areas.

Thoughts on switching to learning Computer Vision

Switching to learning Computer Vision is a promising choice, but it also comes with its share of challenges. This field is exploding, with real-world applications holding immense potential, from automating manufacturing processes to enabling more accurate medical diagnoses. However, the requirement for in-depth knowledge of Machine Learning, Deep Learning, and advanced programming skills, along with substantial investment costs, pose significant hurdles. You need to carefully consider your abilities, passion, and career goals before making a decision. If you’re ready to learn, persevere, and are passionate about exploration, Computer Vision can be a path that unlocks numerous opportunities for your career.