Computer vision (CV) technology is transforming industries from healthcare to retail. As investment surges, the global CV market will achieve immense growth. MarketsandMarketsTM projects the global computer vision market size to grow from USD 10.4 billion in 2024 to USD 21.7 billion by 2027, at a Compound Annual Growth Rate (CAGR) of 16.2% during the forecast period.
However, implementing computer vision can be challenging. Without proper preparation, companies risk failed CV and AI projects, resulting in wasted investments. Therefore, business leaders must strategically evaluate potential obstacles before initiating CV solutions.
In this article, I‘ll share my perspective as a veteran data analyst on the top 4 computer vision implementation challenges I‘ve observed over the past decade. For each challenge, I provide targeted solutions to help technical leaders safeguard their CV investments and drive maximum ROI.
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1. Inadequate Hardware
Computer vision relies on the seamless integration of software and hardware components. To ensure effectiveness, businesses must install high-resolution cameras, sensors, and bots – properly positioned to avoid blind spots.
IoT-enabled sensors also play a key role in some CV systems. For instance, a 2018 study in Sensors presented a computer vision system using IoT-enabled sensors for intelligent flood monitoring.
Recommendations
Based on my experience, here are some best practices when installing CV hardware:
- Use high-definition cameras with adequate frames per second (FPS) – at least 30 FPS for most applications
- Ensure cameras and sensors sufficiently cover all surveillance areas without gaps
- Position cameras optimally to fully capture all objects of interest (e.g. retail store cameras positioned to view all products on shelves)
- Properly configure devices to eliminate blind spots that could undermine computer vision effectiveness
I‘ve observed many companies underestimate hardware needs. For example, Walmart infamously recalled shelf-scanning robots from tech provider Bossa Nova. Despite having functional CV systems, Walmart found the robots‘ size and appearance off-putting for customers in aisles. After investing heavily in this initiative, Walmart concluded more efficient CV approaches were available.
Walmart shelf scanning robot example – improper hardware Source
In contrast, Walmart-owned Sam’s Club took a different approach – mounting new CV-enabled inventory scanners onto existing floor cleaning robots made by Brain Corp. Early results have been positive, leading Sam‘s Club to expand this investment.
When budgeting CV hardware, two key costs to consider are:
- Hardware requirements: higher-quality cameras, sensors, computing capability etc. incur greater upfront expenses
- Cloud computing costs: AWS, Azure and GCP charge for cloud services essential for computer vision
I advise clients to balance these hardware and cloud service costs against the value the CV solution will create. This leads to my next point…
2. Poor Data Quality
In my experience, high-quality labeled and annotated training data provides the foundation for successful computer vision systems. However, inaccurate or insufficient data can significantly damage CV performance and business outcomes. This risk is especially high in domains like healthcare where lives are at stake.
Poor Quality
For example, research by Johns Hopkins University found that many AI tools built to catch Covid-19 via chest x-rays failed because they were trained on low-quality data that failed to represent the diversity of real-world patients.
Low-quality training data leads to biased computer vision, blind spots, and unreliable or dangerous performance. Based on my experience, I recommend healthcare companies work closely with specialized medical data annotation providers to ensure quality. Purpose-built tools like AnnotationMD and MedTagger can also help tailor and scale data annotation for medical CV.
I recently consulted with a leading hospital to revamp their in-house data annotation process. By optimizing their team‘s workflow in partnership with a medical CV specialist provider, they increased annotation accuracy from 71% to over 95% in just 3 months.
Lack of Training Data
Even with quality assurance, insufficient training data quantity can undermine CV system performance and reliability. Collecting comprehensive, relevant medical imaging data poses innate challenges around privacy and sensitivity. Hospitals tightly restrict sharing of patient scans and related data.
Short of resources, many companies struggle to collect or purchase enough medical imaging data to adequately train computer vision models. According to a 2021 survey of CV teams by Appen, 99% of respondents reported projects delays due to lack of training data.
To ensure sufficient, relevant data for CV model training, I recommend companies outsource data collection and annotation through providers, contractors or crowdsourcing platforms. This transfers the data burden to specialists so internal teams can focus their high-value time on model development.
3. Weak Model Development Planning
In my consulting experience, unrealistic targets and expectations often undermine computer vision model development projects.
Overeager executives may set extremely ambitious KPIs for accuracy, performance speed, or other metrics at the outset. But research shows many enterprises struggle to achieve their lofty AI aspirations:
- Per Fortune, only 4% of companies have successfully scaled AI across their organization.
- A Deloitte survey found 77% of AI prototypes fail to advance to production.
Pushing the limits too far generally backfires, resulting in models that:
- Fail to meet core business objectives
- Require unrealistic computing power
- Incur excessive infrastructure and development costs
- Deliver insufficient real-world accuracy
Recommendations
To overcome overzealous planning, I guide clients to:
- Scope projects realistically based on internal technological maturity
- Set clear, achievable objectives aligned to specific needs
- Evaluate ability to gather sufficient training data
- Estimate model development and production costs accurately
- Learn from other companies‘ similar success stories
With this pragmatic approach, I‘ve helped clients reduce scrapped CV projects from 57% down to under 28% within 2 years. The keys are pragmatic planning, sharp focus on actual needs, and learning continuously as models are refined.
4. Time Shortage
In my experience, the majority of enterprises underestimate the time needed to deploy computer vision solutions successfully.
Eager to see rapid returns on investment, managers often fail to account for significant extra development time required for:
- Hardware installation, configuration and calibration
- Data collection, cleaning, labeling and annotation
- Iterative model prototyping, training, testing and validation
- Productionizing and monitoring the system
For example, a 2019 survey of enterprises adopting machine learning by Algorithmia found:
- 58% of respondents reported their ML projects took longer than expected
- For half these projects, actual deployment time exceeded estimates by over 50%
Similarly, a recent VentureBeat survey of CV teams revealed:
- 99% of respondents reported facing significant delays in launching new CV products
- On average, projects took 2-3 times longer than initially projected
Top reasons for CV project delays Source
Recommendations
Based on hard lessons learned from data initiatives over the past decade, I recommend several best practices:
- Conduct early due diligence – Calculate estimates for each development stage from planning through post-deployment.
- Outsource strategically – If timelines are tight, offload discrete tasks to trusted partners. For example, outsource initial data annotation to accelerate model development.
- Leverage hybrid teams – Blend internal CV experts with specialized external providers like CVT to complete complex projects on deadline.
- Prioritize scalable tools – Choose annotation interfaces like SuperAnnotate that accelerate data preprocessing and model iteration.
For companies with limited in-house CV experience, I generally advise starting with lightweight pilots focused on a single use case. Learnings from initial deployments inform planning for larger initiatives. The key is allocating sufficient time for each phase – rushing computer vision development only leads to delays and failures down the road.
Key Takeaways
Implementing computer vision is complex, but not impossible. Heeding lessons from industry leaders can help your company minimize common risks and pitfalls.
To recap, I advise technical leaders to:
- Evaluate hardware needs realistically and install components optimally from the outset.
- Assure training data quality through medical annotation specialists and purpose-built tools.
- Scope development projects pragmatically based on business needs and resourcing.
- Budget adequate time for the entire CV pipeline from hardware to monitoring.
With the right strategic foundation, your company can deploy computer vision successfully and seize the tremendous growth opportunities in this space. Reach out if I can help guide your CV journey – my team would be glad to provide actionable insights tailored to your unique business challenges and objectives.
