Top 4 Computer Vision Challenges & Solutions in 2024

Computer vision (CV) holds great promise but also poses major implementation hurdles. Based on my experience, the top 4 challenges CV teams face are: inadequate hardware, poor data quality, weak planning, and time shortages.

In this article, I‘ll provide an in-depth look at each challenge, real-world examples, data, and most importantly – actionable solutions you can apply. My goal is to help you avoid painful mistakes and accelerate your organization‘s CV success. Let‘s get started!

Introduction

First, what exactly is computer vision and why does it matter? Computer vision refers to algorithms that can identify, classify, and understand visual content like images and videos. It powers use cases like:

• Automated quality inspection in manufacturing
• Assisted driving and navigation in self-driving cars
• Facial recognition for security and surveillance
• Medical diagnosis through scan and pathology analysis
• Automated checkout and inventory management in retail

According to MarketsandMarkets, the global CV market will multiply from $10.4 billion in 2022 to $44.4 billion by 2027. Rapid growth is being fueled by advances in deep learning andneural networks coupled with increased data generation and camera adoption.

However, while interest and investment in computer vision surges, many organizations struggle turning CV proof-of-concepts into full scale deployments. A survey by Algorithmia found 69% of companies exceeded their expected model deployment timelines.

To help you successfully navigate common pitfalls, I‘ll provide an insider‘s look at the top 4 computer vision challenges and proven solutions.

Challenge #1: Inadequate Hardware

The hardware used to capture and process visual data is a foundational component of any computer vision system. Subpar cameras, sensors, processors or IoT devices can totally derail CV projects.

Common hardware issues include:

• Low resolution cameras leading to poor image quality
• Improper positioning or configuration creating blindspots
• Lack of sensors resulting in incomplete data
• Outdated processors that cannot support complex neural networks

Walmart‘s early attempt at in-storerobots for inventory scanning provides a cautionary tale. The robots technically functioned but had two flaws:

1. Large, strange appearance that drew customer complaints
2. Limited mobility preventing them from navigating the entire store

As a result, Walmart ended the program after 6 months despite significant upfront investment. This highlights the importance of selecting hardware optimized for the specific use case.

So how can you choose suitable computer vision hardware?

Here are 4 tips based on my experience:

1. Involve experts early – Consult solutions providers to determine required camera, sensor, and computing specifications. Bring them in during initial scoping.
2. Simulate real-world conditions – Test camera positioning, angles and image quality under projected operating conditions before large scale deployment.
3. Consider scalability – Ensure hardware selections allow room for expansion as application and data complexity increases over time.
4. Compare cloud vs. on-premise – Weigh pros and cons of purchasing infrastructure vs. cloud computing power.

To illustrate, let‘s examine sample hardware costs for an inventory management CV system:

While significant, investing in the right hardware is essential for CV success.

Challenge #2: Poor Data Quality

Machine learning models are only as accurate as the data they are trained on. Low quality training data leads to low quality model performance.

Two common data issues are:

1. Insufficient training images and video to properly train CV models. Collecting domain-specific visual data can be very difficult and time consuming.
2. Flawed or incorrect labels – With medical images for example, inaccurateLabels can have dire consequences if used to train diagnostic models.

A 2021 CV report found data issues to be the number one factor slowing project progress – with 99% of respondents citing it as a blockade.

Let‘s examine two examples of data quality issues and their impact:

• A startup building AI for satellite image analysis lacked sufficient labeled data on rare objects like cruise ships and oil rigs. As a result, their model performed poorly at recognizing these important classes.
• A team creating AI to detect COVID-19 from chest X-rays failed to validate the accuracy of source datasets. Errors in early training data led models to misdiagnose up to 80% of pneumonia cases as COVID.

How can you ensure quality training data for your computer vision initiatives? Here are 3 recommendations:

1. Crowdsource labeling through services like Scale AI and Amazon SageMaker Ground Truth.
2. Work with industry experts – For medical applications, partner with clinicians to label and validate your datasets.
3. Clean as you go – Continuously monitor incoming data for errors using tools like Labelbox.

With quality data, you enable your CV models to learn effectively and deliver reliable, accurate results.

Challenge #3: Weak Model Development Planning

Once hardware and data is addressed, the next step is creating the machine learning models that will power the CV system. This stage also poses pitfalls due to:

• Lack of clear objectives
• Insufficient understanding of data and infrastructure limitations
• Overly complex models that demand unrealistic resources

As a result, many companies find their models take far longer to develop and operationalize than expected. A survey found only 15% of ML projects make it to production due to these issues.

For example, a luxury hotel planned to create a complex model to identify VIP guests and surface their profiles as they entered the lobby. But after 6 months of development, they found:

• Model accuracy was insufficient due to limited guest photos.
• Latency was too high to process guests in real-time.
• The privacy controls and compliance requirements made it untenable.

They wasted significant time and money on an unrealistic project. With better planning, this could have been avoided.

Here are 3 tips to set your model development efforts up for success:

1. Maintain focus on the minimum viable product (MVP) – avoid unnecessary complexity early on. Start simple.
2. Be conservative in assumptions of available data, infrastructure, and team skills. It‘s always easier to scale up.
3. Work backwards from the end goal to scope milestones – know ahead of time what success looks like.

Advanced techniques like Google‘s 480 million parameter Noisy Student model require immense data and computing resources. Ensure your early models are sized appropriately.

Challenge #4: Underestimating Time Requirements

The final challenge I see again and again is failing to budget sufficient time for computer vision initiatives. While model development grabs focus, other activities often take even longer:

• Hardware procurement, installation, and configuration
• Data collection, cleaning and annotation
• Model iteration cycles for tuning and optimization

These tasks can easily double or triple initial timelines if not properly planned for. According to an Algoia survey, the average model goes through 5 major iterations from prototype to production – each taking weeks or months.

Rushing to meet unrealistic deadlines often leads to:

• Significant added costs from rushing data annotation or leaning on external vendors to accelerate development.
• Cutting corners such as reducing validation testing that causes issues down the road.
• Overall lack of due diligence that creates technical debt and rework.

So what can you do to estimate timelines accurately?

1. Break down every project stage and create a detailed task list.
2. Buffer initial estimates – assume tasks will take longer than you expect.
3. Identify dependencies across tasks to model critical paths.
4. Involve team members to estimate durations based on experience.
5. Continuously track progress to keep schedule updated.

While it‘s tempting to be overly optimistic on timelines, taking a conservative approach upfront is prudent.

Key Takeaways

Computer vision systems offer tremendous value but also have pitfalls. By focusing on robust hardware, high-quality data, achievable development plans, and realistic timelines, you can avoid common issues. Don‘t let the challenges deter you from capitalizing on the incredible potential of CV. With diligent planning and execution, it is possible to deploy CV that delivers transformative capabilities and outsized ROI.

I hope these insights on successfully navigating the top computer vision challenges help position your organization for success. Please reach out if you need any assistance with your CV initiatives – I‘m always happy to help!