Introduction

Computer vision has the power to transform businesses by automating visual data analysis. But implementing CV systems comes with challenges. Without the right strategy, projects fall short of expectations.

In this post, I‘ll share insider best practices to set your CV initiative up for success. With over a decade of experience optimizing computer vision for Fortune 500 companies, I‘ve seen firsthand what works.

Follow these tips to sidestep pitfalls and achieve the high accuracy needed to drive real ROI. Let‘s dive in!

The key ingredient for any computer vision system is quality training data. My clients often underestimate how much data is required to achieve state-of-the-art accuracy.

For context, some of the most advanced computer vision models are trained on hundreds of millions of images, videos, and text documents.

For example, Microsoft‘s computer vision API has been trained on over 10 million YouTube videos and 1 billion images. Google‘s model has ingested over 100 million photos and videos.

Failing to provide sufficient training data leads to high error rates and unreliable performance. Unfortunately, collecting and labeling millions of samples in-house is impractical for most organizations.

Outsource Data Annotation

My recommendation is to partner with a professional data annotation firm. I‘ve seen outsourcing boost model accuracy dramatically versus attempting in-house labeling.

Specialty data annotation vendors have several advantages:

• Expert labelers familiar with your specific use case
• Advanced labeling interfaces, tools, and quality assurance protocols
• Scalability to handle millions of samples quickly
• Data security and compliance best practices

Choosing the right partner is critical. For a medical computer vision application, I would recommend a firm focused on medical images like radiology scans or pathology slides. They will have more relevant expertise than a generalist annotation shop.

Synthetic Data Generation

An emerging technique called "synthetic data generation" can also help. AI algorithms create artificial training data that looks convincingly real.

Research shows that augmenting real-world data with synthetic data can reduce the number of manually annotated samples needed, while still maintaining accuracy.

For example, startups like Anthropic and InstaDeep use synthetic data to train computer vision for autonomous vehicles. This slashes the manual effort of capturing millions of real-world miles of driving footage.

However, for many applications, genuine annotated data remains ideal currently. Weigh the pros and cons of synthetic data for your specific use case.

The hardware used to capture images and video is another key element. Low-quality cameras lead to noisy inputs that hamper computer vision accuracy.

Carefully evaluate cameras, sensors, and computing devices to ensure your hardware matches performance requirements.

Camera Considerations

When recommending cameras, I consider 4 key attributes:

• Resolution – Higher resolution (1080p or 4K) captures fine details essential for accuracy.
• Frame Rate – Higher FPS (60+) produces smooth video for tracking motion.
• Field of View – Must fully cover the area to be monitored without gaps.
• Low Light Capabilities – Enables operation in dark environments.

Industrial cameras meant for manufacturing and warehousing often outperform consumer-grade models. They are built to withstand hazards like dust, moisture, and vibration.

Expect to invest $200-$500 per camera in an industrial computer vision system. High-end models for critical applications can cost over $1000.

Compute Power

A high-performance computer vision platform requires serious computing horsepower, especially for real-time inference.

Commercial vendors like NVIDIA, Intel, and Google offer optimized hardware and cloud solutions specifically for computer vision.

For example, Google‘s Edge TPU chips offer blazing fast inferencing that can analyze imagery milliseconds after it‘s captured. This is ideal for applications like instant defect detection in manufacturing.

On-premise servers with high-end GPUs or dedicated AI accelerator cards work well for low-latency applications with strict data security needs.

Sensor Selection

Beyond cameras, additional sensor data often improves computer vision performance. For instance, an industrial robot that "sees" can incorporate torque or vibration data from motor sensors to better understand its surroundings.

Work closely with your developer to identify supplemental sensor data that could enhance accuracy or provide redundancy.

Developing and deploying computer vision represents a substantial investment. A detailed ROI analysis is strongly advised before moving forward with any CV initiative.

I guide clients through an ROI assessment using the following framework:

Estimate Total Costs

• Hardware (cameras, compute, sensors)
• Software licenses
• Integration and deployment
• Data collection, annotation, storage
• Model development and training
• Ongoing monitoring and maintenance

These costs can easily reach 6 or 7 figures for enterprise computer vision capabilities.

Project Tangible Benefits

• Increased output or yield
• Reduced operational and labor expenses
• Improved quality and compliance
• Optimized customer intelligence
• Enhanced safety and security

Compare Costs vs. Benefits

Aim for a 12-18 month ROI or faster to justify the investment. If the numbers don‘t add up, revisit requirements or reconsider computer vision.

Here is an example ROI analysis I created for a manufacturing client exploring a custom CV solution for automotive inspection:

Walking through this exercise provides critical visibility into the business case before major commitments are made.

Very few organizations can tackle custom computer vision engineering entirely in-house. I typically recommend partnering with an experienced vendor.

When evaluating suppliers, look for demonstrated expertise in your specific industry vertical. This table summarizes the specializations I see among top firms:

Industry	Leading CV Specialists
Manufacturing	Intellias, EdgeHill, Rad AI
Automotive	DeepVision, Embotech, Argo AI
Healthcare	Quantib, Subtle Medical, Enlitic
Retail	CogniCor, SightCorp, Sparks.ai

Beyond industry experience, assess their technical capabilities across:

• Data Management – Can they handle data at enterprise scale?
• Algorithm Expertise – Do they utilize leading open-source libraries effectively?
• Model Training – Are they skilled at iterative training for accuracy?
• Deployment & Integration – Can they operationalize smoothly in your environment?
• Cloud vs. On-Premise – Do they support different deployment models?

Taking the time to find the right partner will pay dividends throughout your computer vision program.

While you certainly don‘t need to be an engineer, having a high-level grasp of popular techniques will help discussions with your partner.

Below I break down 4 commonly used approaches at a basic level:

Image Classification

• Categorizes images based on their contents
• Example: Labeling products on an assembly line as defective or non-defective

Object Detection

• Identifies objects within images and localizes them with bounding boxes
• Example: Detecting people entering a restricted area

Object Tracking

• Follows detected objects across video frames as they move
• Example: Tracking vehicles to analyze traffic patterns

Image Segmentation

• Divides images into sections to isolate regions of interest
• Example: Identifying damaged portions of an aircraft fuselage for repair

Of course, many other sophisticated techniques exist. But these 4 represent a solid introductory overview.

Implementing computer vision successfully involves much more than just coding a deep learning model. Follow these best practices to set your initiative up for success:

• Secure ample training data – Work with data annotation experts to collect high-quality labeled datasets.

• Select hardware purposefully – Choose cameras, sensors and compute to match performance needs.

• Confirm positive ROI – Validate the business case with a detailed cost/benefit analysis.

• Partner strategically – Find a CV developer with proven expertise in your industry.

• Learn the fundamentals – Pick up enough CV know-how to hold your own in discussions.

Adopting these suggestions will help you avoid missteps and unlock the tremendous potential of computer vision. Want to dig deeper? Check out my related articles on the Aimultiple blog:

• Computer Vision Developer Guide – Comprehensive overview of popular CV techniques
• Computer Vision Use Cases – Examples across manufacturing, retail, automotive and more
• How to Hire Computer Vision Experts – Guide to evaluating and partnering with CV suppliers

I hope these insider tips help pave the way for a successful computer vision program. Please reach out if you need any additional guidance!