Computer vision (CV) is revolutionizing industries, from autonomous vehicles to healthcare, but success depends critically on the collection of high-quality image data. Organizations that implement strategic data collection services can achieve higher accuracy in specialized applications, while poor data strategies lead to biased models and compliance violations.
See what image data collection is and how business leaders and developers can gather relevant image data:
What is image data collection for AI?
Image data collection for AI/ML training involves gathering and preparing images to be added to datasets that will train AI/ML algorithms.
This can include images of people, animals, objects, locations, etc. For instance, a CV-based system for detecting the quality of fruits on a conveyor belt might require training on thousands of images. Such datasets can be large or small, depending on the project’s scope.
Here is a sample dataset for a quality control computer vision system that scans apples.
An image dataset for a facial recognition system might look something like this (only larger):
Data collection maturity framework:
- Level 1: Ad-hoc manual collection (< 1,000 images)
- Level 2: Systematic manual processes (1,000-10,000 images)
- Level 3: Semi-automated collection (10,000-100,000 images)
- Level 4: Fully automated with quality controls (100,000-1M+ images)
- Level 5: AI-driven collection optimization (1M+ images with active learning)
If you wish to work with an image data collection service, click here.
Challenges in image data collection
Gathering image data can be costly
A computer vision system requires a large number of images to be trained; however, this varies depending on the project’s scope.
According to a study1 , a facial recognition system with 300K and 400K images will have significantly higher accuracy levels than those trained with 100K and 200K.
However, collecting datasets of this magnitude, as helpful as they might be, requires expensive cameras and an additional workforce.
Figure 1. Facial recognition accuracy with different dataset sizes
For instance, a facial recognition system being deployed in different countries will require data from the population of those specific countries. If this is done in-house, it can raise the project’s budget to unreasonable heights.
Gathering images can have ethical and legal constraints
Gathering images can sometimes involve ethical and legal considerations. For instance, a facial recognition system might require face data for training. However, since face images are considered biometric data, they can be challenging to collect and use legally without proper consent frameworks.
Other biometric image data that computer vision systems can gather includes fingerprint images, retina scans, etc.. If companies fail to adhere to ethical and legal considerations, they may incur expensive lawsuits and regulatory penalties, ranging from thousands to billions of dollars.
Watch this video to learn about one of Facebook’s lawsuits on unethically collecting the user’s biometric data:
Gathering data of any sort can be biased
Another issue when collecting data is the risk of the dataset becoming biased. This unconscious bias is transferred from the collector to the dataset and then to the AI/ML model that’s being trained.
Common types of data bias:
- Geographic bias: Over-representation of specific regions or demographics
- Temporal bias: Data that doesn’t account for seasonal or time-based variations
- Selection bias: Non-random sampling that misses key populations
- Annotation bias: Inconsistent labeling standards across different annotators
- Confirmation bias: Collecting data that confirms existing assumptions
Real-life bias examples:
- Facial recognition systems perform poorly on darker skin tones due to training data predominantly featuring lighter skin
- Medical AI systems show lower accuracy for female patients when trained primarily on male patient data
- Autonomous vehicle systems are struggling with pedestrian detection in non-Western clothing styles
Best practices to consider while collecting image data
Leverage automation with quality controls
Leveraging automation to collect image data can reduce bias because the data collected will be random and free from prejudice and discrimination. You can program a bot or leverage an automated data collection application to add data to your existing dataset automatically.
Automated Collection Strategies:
- Web scraping with filters: Automated collection from public sources with quality and relevance filters
- IoT sensor networks: Cameras and sensors that collect data based on predefined triggers
- Crowdsourcing platforms: Automated distribution of data collection tasks to a global workforce
- Synthetic data generation: AI-generated images to supplement real-world datasets
However, this can create storage and quality issues, as datasets will continue to increase in size. Setting parameters to regulate collection volume, implementing quality gates, and establishing data retention policies are essential.
Leverage Crowdsourcing
Crowdsourcing can overcome many of the traditional challenges associated with data collection. Since the crowdsourcing model works with data collectors worldwide through micro-job mechanisms, the collected data can be more diverse and representative.
Strategic Crowdsourcing Implementation:
- Demographic targeting: Establish quotas for age, gender, ethnicity, and geographic distribution
- Quality incentives: Performance-based payment structures rewarding high-quality contributions
- Training protocols: Standardized training for crowdsource workers on annotation standards
- Validation workflows: Multi-level review processes with expert validation
Crowdsourcing to collect data is a way to bypass AI bias by ensuring diverse representation across multiple demographic categories.
Ensure consistency
Image data consistency is essential for determining model performance levels. Standardization protocols must cover technical specifications, annotation guidelines, and quality metrics.
Ensure quality
The quality of the overall image dataset is evaluated through multiple dimensions that directly impact model performance:
- Relevance to the scope of the project
- Comprehensiveness so that it can cover all requirements of the AI/ML model
- Authenticity and validity
To learn more about data collection quality assurance, check out this quick read.
You can also check our data-driven list of data collection/harvesting services to find the option that best suits your needs.
Real-life case studies
Case study 1: Tesla’s autonomous driving data collection
The challenge: Tesla aims to revolutionize transportation by making roads safer, reducing congestion, and increasing energy efficiency through autonomous driving, which requires massive amounts of real-world driving data.2
The solution: Tesla utilizes a HydraNet architecture, comprising a single giant neural network that handles all tasks. This neural network is trained with PyTorch and a pool of workers to accelerate the results. A complete loop is implemented: drivers collect data, Tesla labels that real-world data, and trains the system.
Results3 :
- Data volume: Over 1 million miles of driving data collected daily from Tesla’s fleet
- Technology: Tesla Vision system equipped only with cameras, making it one of the only companies in the world not to use RADARs
- Processing power: Tesla Dojo supercomputer explicitly designed for computer vision video processing and recognition to train machine learning models for Full Self-Driving (FSD)
- Methodology: Tesla collects data from its cars on varying road conditions and traffic trends in different parts of the world. Tesla uses algorithms to reconstruct lanes, road boundaries, curbs, crosswalks, and other images
Case study 2: Amazon Go’s computer vision revolution
The challenge: Create a completely automated retail experience where customers can “just walk out” without traditional checkout processes. 4
The solution: Amazon utilizes computer vision, deep learning algorithms, and sensor fusion, much like those found in self-driving cars, known as “Just Walk Out” technology.
Technical implementation:
- Multi-modal data collection: Stores use technologies such as computer vision, sensor fusion, and deep learning to record and analyse digital inputs continuously
- Data sources: Ceiling-mounted cameras, weight sensors, and shelf sensors
- Processing: Computer vision automatically tracks items added to a shopping cart and charges customers accordingly as they leave the store
Results5 :
- Store network: 28+ Amazon Go locations (as of 2024)
- Data processing: Real-time analysis of thousands of customer interactions daily
- Accuracy: 98%+ accuracy in item tracking and billing
Case study 3: Google’s diabetic retinopathy detection system
The challenge: Screen 70 million people at risk for diabetic retinopathy when “it’s not humanly possible to screen these 70 million” patients manually.6
The solution: Working closely with doctors in both India and the US, Google created a development dataset of 128,000 images, each of which was evaluated by 3-7 ophthalmologists from a panel of 54 ophthalmologists. This dataset was used to train a deep neural network to detect referable diabetic retinopathy.
Data collection methodology:
- Expert validation: Each image is reviewed by 3-7 specialist ophthalmologists
- Geographic diversity: Data collected from India and the United States
- Quality control: 54-member expert panel ensuring annotation consistency
- Rare disease focus: Targeted collection of diabetic retinopathy cases
Results7 :
- Dataset size: 128,000 high-quality retinal images
- Accuracy: More than 90% accuracy, and can give a result in less than 10 minutes
- Global impact: Deployed in Thailand and India for screening programs
Case study 4: John Deere’s precision agriculture
The challenge: Optimize crop monitoring and precision farming across millions of acres with varying conditions, soil types, and crop varieties.
The solution: Drone-based multispectral imaging combined with ground sensor networks to create comprehensive agricultural datasets.
Technical implementation:
- Data sources: Multispectral cameras (RGB + infrared), soil moisture sensors, weather stations
- Collection method: Automated drone flights collecting 1,000+ images per field per season
- Processing: AI-powered crop health analysis identifying disease, pest damage, and nutrient deficiencies
- Integration: Real-time data fusion with farming equipment for precision application
Results:
- Coverage: Over 100,000 farm fields monitored globally
- Data volume: Millions of crop images collected annually
- ROI: 15-20% reduction in pesticide use, 10-15% increase in yield optimization
- Adoption: Used by farmers managing 50+ million acres worldwide
Case study 5: Zebra Medical Vision’s FDA-approved AI
The challenge: Create an FDA-approved medical imaging AI that can detect multiple conditions from routine medical scans with radiologist-level accuracy.
The solution: A Comprehensive medical imaging dataset covering multiple pathologies with strict regulatory compliance protocols.
Data collection framework:
- Multi-institutional collaboration: Partner hospitals across four continents
- Pathology coverage: X-rays, CT scans, MRIs covering 10+ medical conditions
- Annotation protocol: Board-certified radiologists with specialty training
- Compliance: HIPAA, GDPR, and FDA regulatory frameworks
Results:
- Dataset size: 2+ million medical images across multiple modalities
- FDA approvals: 7 different AI products approved for clinical use
- Clinical impact: Deployed in 1,000+ medical facilities globally
- Accuracy rates: 94-98% sensitivity/specificity across different pathologies
Image data collection use cases
1. Healthcare
Hospitals use federated learning to train diagnostic AI models on decentralized MRI/X-ray datasets without sharing sensitive patient data. Synthetic datasets (e.g., AI-generated tumor scans) supplement cases of rare diseases.
- Tools: NVIDIA Clara, Owkin.
- Federated learning platforms: PySyft, NVIDIA Flare
2. Retail
Virtual try-on systems require hyper-diverse datasets covering 50+ body types, skin tones, and cultural apparel. Retailers use 3D body scans from in-store kiosks and crowdsourced selfies (with consent).
- Tools:
Vue.ai is used for personalized styling datasets, and Zeekit (acquired by Walmart) is used for real-time AR try-ons. - 3D scanning: Styku, TrueKit
3. Agriculture
Drones equipped with multispectral cameras capture crop health data (RGB + infrared), which is then fused with soil moisture sensors for precision farming. Farmers share anonymized data via agricultural data cooperatives to train community AI models.
- Tools: DJI Agriculture drones, MicaSense sensors, Climate FieldView platform
4. Traffic management system
Traffic management systems collect anonymized CCTV feeds and scans from autonomous vehicles to optimize routes and traffic flow. Privacy is maintained through edge processing, where data is anonymized on-device before transmission.
- Tools:
NVIDIA Metropolis for video analytics, Intel OpenVINO for edge processing, and Cisco smart city platforms
5. Manufacturing
Quality control systems use ultra-high-resolution thermal cameras to detect micro-cracks in materials. Synthetic defect data simulates rare production-line failures.
- Tools:
Cognex VisionPro and Siemens Synthetic Defect Generator, Keyence vision systems, Basler industrial cameras
Further reading
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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