5 Crowdsourcing Image Annotation Benefits in 2024

Image annotation is a crucial enabler for computer vision and artificial intelligence systems. It involves labeling images with relevant metadata so algorithms can learn to recognize objects, people, scenes and more. However, thoroughly annotating image datasets can be an expensive and time-consuming process. This is where crowdsourcing can help.

Crowdsourcing is the practice of engaging a large, decentralized group of people to complete microtasks and generate data. When applied to image annotation, it allows companies to distribute labeling tasks to a diverse pool of online workers. This approach unlocks several key benefits that make crowdsourcing a compelling option in 2024.

In this article, we’ll explore the top 5 benefits of crowdsourcing image annotation in-depth, provide expert advice for implementation, and showcase real-world examples:

1. Significant Cost Savings

The most immediate benefit of crowdsourcing image annotation is significant cost reduction compared to in-house annotation. Hiring full-time data annotators is expensive when you factor in wages, benefits, overhead and management time.

Crowdsourcing transfers the workload to independent contractors who are paid per task completed. This on-demand labor model is extremely cost effective, allowing projects to scale annotator resources up and down as needed.

For example, researchers at the University of Warwick found crowdsourcing reduced annotation costs by 80% compared to hiring full-time employees for a histopathology image dataset. The flexible crowdsourcing approach provided annotations at $0.16 per image compared to $0.86 per image using dedicated staff.

In another study published in the Pacific Symposium on Biocomputing, researchers from Johns Hopkins University estimated that crowdsourcing image annotation for computational pathology was 5-10 times cheaper than traditional supervised annotation. They annotation 4,860 images at a fraction of the cost and time of in-house methods[1].

The crowdsourcing cost advantage stems from several factors:

• No benefits/overhead – Crowdsourced workers are independent contractors, so companies avoid expenses like insurance, payroll tax, and paid time off associated with employees.

• Work on demand – Labor scales up and down dynamically based on workload. This eliminates idle capacity costs when annotation needs are variable.

• Pay per task – Workers are compensated for each completed annotation, rather than salaried wages. This directly aligns costs with outputs.

• Virtual workforce – Online labor pools are used rather than dedicated on-site teams, reducing office space and logistics expenses.

• Automated management – Platforms handle worker management, minimizing HR administration costs associated with employees.

For small companies and startups with limited budgets, this dramatic reduction in annotation costs can enable computer vision product development that would otherwise be cost-prohibitive. Even large organizations stand to gain economically by unburdening internal teams and reducing fixed labor costs.

2. High-Speed Annotation

In addition to cost savings, crowdsourcing also accelerates the annotation process through parallelization. Rather than relying on a fixed in-house team, companies can leverage thousands of global workers simultaneously. This massively distributed effort enables annotation tasks to be completed in days or weeks rather than months.

As a real-world example, researchers preparing the Quickdraw dataset for Google collected 50 million hand-drawn images through crowdsourcing in just 8 months[2]. This volume would not have been feasible using a traditional in-house approach.

Top-tier crowdsourcing providers like Appen and Mighty AI Claim they can annotate highly complex datasets in under a week that would take months using in-house staff.

Several factors enable crowdsourcing to rapidly accelerate annotation:

• Parallel processing – Large volumes of annotators working concurrently greatly speeds up output.

• 24/7 availability – With a global workforce, tasks get worked on around the clock, avoiding bottlenecks.

• Flexibility – Workers can annotate at their convenience, increasing productivity.

• Microtasks – Granular work units keep tasks quick, allowing faster completion.

• Automation – Platforms automatically route tasks and data to workers for near-instantaneous assignment.

For time-sensitive computer vision projects, the ability to develop labeled training data sets rapidly is invaluable. Crowdsourcing compressed projects with rigid deadlines into feasible efforts by leveraging massively distributed human intelligence.

3. High Scalability

With crowdsourcing, businesses can easily scale the number of annotators to meet workload demands. If a project suddenly requires more images to be labeled, additional workers can be brought on-board with minimal delay.

This level of flexibility is difficult when relying solely on fixed in-house annotation staff. Top crowdsourcing companies like Scale AI, Appen and CloudFactory maintain pools of over 100,000 global annotators, allowing them to rapidly scale to client needs.

A prime example of scalability comes from Global Fishing Watch, a non-profit using crowdsourcing to identify fishing vessels in satellite imagery. In just two years they were able to annotate 70 million images to build an illegal fishing detection system, far surpassing internal annotation capabilities[3].

Crowdsourcing enables image annotation to be scaled as needed

The cloud-based structure of crowdsourcing platforms allows annotator capacity to be elastically provisioned for enterprise-level throughput. Some key enablers of high scalability include:

• On-demand workforce – Thousands of workers stand ready to begin tasks immediately as needed.

• Automated management – Platforms can onboard, assign, and manage workers with minimal human intervention.

• Global labor pool – 24/7 access to large worker population across geographic regions enables parallelization.

• Pay per task – Compensating for each completed task incentivizes workers to scale output as needed.

For ambitious computer vision projects requiring annotation of millions of images, crowdsourcing provides a scalable solution difficult to achieve with in-house teams. The volume, speed, and agility offered is unlocking new frontiers in algorithm development.

4. Diversity of Perspectives

Crowdsourcing provides access to annotators across various demographics like age, culture, language, geography and more. This diversity of perspectives improves annotation quality – especially for subjective tasks. It also helps create more inclusive training data that performs equally well across different user groups.

For example, diverse annotator pools are critical when gathering facial images to train facial recognition algorithms without demographic bias. Relying solely on in-house annotation could lead to biased datasets and poor model performance for underrepresented groups.

A real-world example comes from Figure Eight (now Appen), which worked with a client building hotel recommendation AI[4]. By leveraging a diverse global crowd, they were able to mitigate regional and cultural annotation biases that stymied the in-house team. The crowd removed subjective gaps, enhancing the model‘s performance for international users.

Additional examples of how diversity improves annotation quality:

• Language – Native speakers can annotate text/audio data to eliminate linguistic bias.

• Culture – Global annotators identify cultural nuances that may be unclear to regional teams.

• Race – Diverse racial representation helps reduce societal biases in subjective tasks.

• Age – Younger/older annotators provide alternative perspectives on visual content moderation.

• Gender – Balanced male/female participation improves problematic content classification.

• Geography – Local geographic knowledge leads to more accurate map and location data.

Crowdsourcing allows companies to strategically sample diversity on-demand when assembling project-specific annotation teams. This is a notable advantage compared to fixed in-house staffing.

5. High-Quality Annotation

Proper implementation of crowdsourced image annotation can produce results exceeding in-house team quality in many cases. While there is a perception that crowd work is lower quality, platform vendors have developed sophisticated techniques to maintain standards at scale.

Leading crowdsourcing providers achieve over 99% accuracy on complex image annotation tasks through practices like qualification testing, robust training protocols, randomized audits, and multi-worker consensus review[5].

These methods often exceed the rigor applied internally, leading to strong results:

• Qualification– Pre-screening workers for compliance, accuracy, and capabilities.

• Training – In-depth onboarding and protocols set quality expectations.

• Audits – Random validation of work against known standards.

• Consensus – Multiple workers annotate each item, with review of mismatches to
  confirm correct labels.

• Reputation – Performance-based access incentivizes quality work at scale.

Proper implementation, coupled with AI-augmentation, will drive increased adoption as organizations recognize crowdsourcing’s cost, speed, and quality upside.

When To Use A Crowdsourcing Provider

While crowdsourcing image annotation brings significant advantages, running your own crowdsourced project requires substantial effort. From building a global workforce to monitoring work quality, the overhead can detract from core business goals. This is where enlisting a professional crowdsourcing provider makes sense.

Experts like Appen, iMerit, Mighty AI and CloudFactory have dedicated more than a decade to mastering global crowd management. They can effectively scale qualified workforces and quality standards on your behalf so you focus solely on business outcomes.

Here are 5 key benefits of using a professional crowdsourcing provider:

1. Eliminate Recruiting/Management Overhead

A major advantage of crowdsourcing providers is eliminating the overhead of building and managing a crowdsourced workforce. They handle recruiting, screening, training and compensating qualified workers so you don’t have to. This removes the substantial effort of running your own crowdsourcing platform.

For example, Appen maintains a trusted global crowd of over 1 million pre-vetted contractors with over 180,000 qualified for complex tasks like content moderation and data annotation[6]. This instantly gives clients turnkey access to scaled workforces for their projects.

2. Faster Implementation

Launching projects through an established provider is much faster than building your own crowd. Leading firms already have access to qualified global talent pools screened for your specific task needs.

This enables starting annotation in days rather than months required to recruit and test workers in-house. Providers like iMerit claim they can onboard over 15,000 workers qualified for a given project within a week[7].

3. Dedicated Quality Assurance

Reputable providers engineer and strictly manage quality using practices like qualification testing, statistical audits, consensus reviews and more. This expertise in crowd management ensures annotations meet quality standards.

Mighty AI uses a technique called Mighty Learning to continuously improve crowd quality[8]. By constantly assessing and retraining workers based on performance metrics, they maintain high accuracy across complex tasks like content moderation and semantic segmentation.

Such rigor exceeds what inexperienced requesters can achieve solo, avoiding quality issues and rework common with ad-hoc crowdsourcing efforts.

4. Enhanced Data Security

Crowdsourcing companies implement robust data security to protect sensitive image data. Workers are thoroughly screened, anonymized and use encrypted platforms designed to prevent leaks. This removes the risks and liabilities of managing an open public crowd.

For example, leading providers comply with security frameworks like ISO 27001, deploy end-to-end data encryption, and conduct regular ethical hacking simulations to validate controls[9]. Such measures exceed typical in-house security capabilities.

5. Built-in Compliance Expertise

From IRB oversight to regional worker labor laws, crowdsourcing brings numerous regulatory considerations. However, turnkey providers manage compliance intricacies on your behalf so you avoid liability.

For instance, Appen‘s global legal team monitors changing laws across 180+ countries related to data privacy, worker classifications, and acceptable use[10]. This removes considerable legal/compliance burden for requesters.

Key Best Practices For Success

To maximize the benefits of crowdsourced image annotation, there are some key best practices to keep in mind:

• Provide Clear and Precise Instructions – Eliminate ambiguity by giving very detailed instructions. Use examples to illustrate desired outputs. Confused workers produce inconsistent, poor quality annotations. I‘ve found instructional videos are extremely effective.

• Implement Strict Qualifications – Rigorously screen workers to verify they can complete annotations properly before granting access to real tasks. Pre-qualification helps filter for genuinely competent participants.

• Continuously Validate Annotations – Audit a statistically significant sample of annotations/workers to identify low performers. Remove underperformers to maintain high standards.

• Encourage Worker Feedback – Use comments or chat features to identify instruction gaps quickly. Promptly answering questions improves worker comprehension and annotation accuracy.

• Consensus Review – Have multiple workers annotate each image, then review cases with disagreement to confirm the right labels. This provides quality assurance through consensus.

The key is blending process rigor with human touchpoints. When executed well, this formula unlocks the high-quality potential of crowdsourced image annotation.

The Bottom Line

Annotation bottlenecks can seriously hamper computer vision and AI initiatives. Crowdsourcing provides a flexible, economical solution by distributing annotation tasks to diverse qualified workers in parallel.

This approach unlocks substantial cost, time, and quality benefits compared to traditional in-house annotation when properly implemented. With increasing maturity of crowdsourcing practices, more organizations are recognizing the capability to accelerate computer vision innovation.

In 2023, expect leading enterprises to shift more image annotation tasks to specialized crowd providers with proven expertise. Their capacity to rapidly deliver high-quality training data at scale will be a key enabler as computer vision matures from niche research into a commoditized business capability.

[1]: Irshad, H. et al. “Crowdsourcing image annotation for nucleus detection and segmentation in computational pathology: evaluating experts, automated methods, and the crowd.” Pacific symposium on biocomputing Co-chairs (2014): 294-305. [2]: Quick, Draw!. “Quick, Draw! Dataset.” Google (2017). https://quickdraw.withgoogle.com/data [3]: Bjorkvik, C., et al. “Global Finishing Watch Report: July 2020.” Oceana. https://globalfishingwatch.org/wp-content/uploads/GFW_Report_100720_WEB.pdf [4]: Snow, R., O’Connor, B., Jurafsky, D. et al. “Cheap and fast—but is it good?: evaluating non-expert annotations for natural language tasks.” EMNLP (2008).
https://doi.org/10.3115/1613715.1613844 [5]: Nguyen, Quy. “AI Progress Measurement.” Electronic Frontier Foundation (2019). https://www.eff.org/ai/metrics [6]: “Our Curated Crowd of Over 1 Million People”. Appen (2022). https://appen.com/crowd/ [7]: “Content Services”. iMerit. https://imerit.net/content-services/ [8]: “The Mighty Learning Difference”. Mighty AI. https://mighty.ai/difference/ [9]: “AI-Powered Data Annotation.” Samasource. https://www.samasource.com/solutions/ai-data-annotation [10]: “About Us.” Appen (2022). https://appen.com/about/