Tesla, a reputed automotive giant, has always been in the headlines, and many times, for all the wrong reasons. You must have heard of the infamous accident of 2016, where a Tesla Model S collided with an 18-wheeler truck, resulting in the passing away of the former driver. All investigations concluded that Tesla’s computer vision system could not detect the white truck taking a turn. This, combined with the driver’s overreliance on autopilot mode, led to the fatality. That’s one classic case of sub-par edge case management in an AI system. And this is just one of many similar incidents reported where automated driving systems failed to accurately detect and identify objects in dynamic scenarios.
For a complex environment like autonomous driving, edge cases such as unusual lighting conditions, unexpected turns, or rare scenarios can lead to catastrophic outcomes if not handled properly.
A heft implication, right? This is why managing and working around edge cases is one of the biggest AI challenges. In this write-up, we’ll explore more in this area and see how involving humans in the loop can make a difference.
Edge Cases Explained
Edge cases are certain outliers or unusual and unexpected cases that AI models may struggle to handle. These scenarios often fall outside the typical distribution of the training data, making them challenging for models to interpret accurately. This is why managing and working around edge cases is a critical part of effective AI model training.
Here is a list of common computer vision edge cases:
- Lighting Issues: Overexposed or underexposed images, shadowy areas, glare, etc.
- Adversarial Noise: Subtle changes in an image, such as distortions or color alterations
- Scale Variations: Objects that look smaller/larger than their actual size
- Image Quality Issues: Distorted, blurry images
- Overlapping Objects: Multiple objects partially covering one another
- Motion Blur: Fast-moving objects, like a speeding vehicle or a running athlete
- Cultural or Regional Variations: Differences in clothing or architectural styles between regions
- Occlusions: Partially hidden objects, such as a pedestrian behind a parked car
How Does Edge Case Management in AI Computer Vision Models Work?
AI edge case management is a comprehensive process involving identifying, managing, and learning from rare scenarios outside the typical data scope.
Identifying and Detecting Edge Cases
In AI computer vision models, edge cases can be detected through anomalies that surface during model deployment. These cases can be identified and detected through:
- Anomaly Detection Mechanisms: You can use ML algorithms to identify patterns and distributions likely to generate an abnormal output.
- Uncertainty Approximation: AI/ML models can be designed to provide confidence scores for their predictions. Low-confidence outputs may indicate potential edge cases, prompting further review.
- Active Learning: Models can also be configured to actively select certain data points for labeling based on pre-determined criteria.
Collecting and Annotating Data
Once you have identified a set of computer vision edge cases, gather instances where the model fails or exhibits uncertainty. This data serves as the foundation for understanding and addressing edge cases.
The collected data is examined to identify common characteristics and sent for further processing. It is then followed by visual data annotation to label edge cases using bounding boxes, segmentation masks, and other classification labels. Annotators also have offer explanations for why the model may have failed, highlighting nuances such as:
- Environmental Factors: Lighting, weather, or background clutter.
- Object Attributes: Size, color, or partial occlusion.
- Scene Dynamics: Motion blur or unusual angles.
Retraining and Validating the Model
The computer vision model is retrained using the annotated visual data to enhance its ability to handle future edge cases. This could involve:
- Tweaking Model Parameters: Fine-tuning the model to improve performance on both standard and edge case data.
- Adding Synthetic Edge Case Data: You can generate synthetic edge case data to see if the computer vision model performs as intended.
- Model Validation: Testing the updated model to ensure it performs well across all scenarios.
Monitoring and Iterating
AI edge case management is not a one-off activity or process; it’s ongoing. Regular monitoring and updates are essential to maintaining model robustness as new edge cases emerge.
Why is Edge Case Handling Critical for AI/ML Model Accuracy?
Addressing these atypical cases is crucial to achieving accuracy and reliability in AI computer vision models.
Reducing Model Bias
AI models are always under the bus for biases, and neglecting or failing to handle these edge cases can significantly contribute to model bias. Particularly in computer vision models, where data is in a visual format (2D or 3D images, videos, etc), a model trained on sub-par image data can lead to misclassification or may fail to account for underrepresented segments.
Preventing Critical Failures in High-Stakes Applications
Missing out on edge case management in AI models can result in costly implications in high-stake applications. For example, a self-driving vehicle failing to identify an unexpected obstacle may make erroneous driving decisions if such scenarios weren’t considered during training.
Gaining User Trust and Adoption
Users are more likely to trust and adopt AI systems that perform reliably in all situations. When models handle edge cases effectively, it fosters greater confidence among users.
The Role of Humans-in-the-Loop (HITL) in Modern Computer Vision Models
Today, edge case management is largely automated, thanks to a number of AI-powered testing and validating tools/platforms. However, while incredibly powerful, these tools often fall short when it comes to a new, unpredictable scenario because:
- They lack proper contextual understanding
Even though AI has come a long way in handling computer vision edge cases, it still lacks the intuitive reasoning that humans possess, especially when dealing with rare objects, partial occlusions, or cultural variations in data.
- They are subject to bias
AI models work as well as training datasets, but the latter often fail to represent the full diversity of real-world scenarios. This creates blind spots, especially for atypical computer vision edge cases, like identifying objects partially hidden in crowded environments (e.g., a short-heighted pedestrian hidden behind a vehicle). If the training data lacks clarity, the AI model won’t flag the object as a person.
- They rely heavily on patterns
AI models are great at spotting patterns because they’re trained on large datasets filled with familiar, recurring examples. But when they encounter something outside those patterns, they don’t perform optimally. This happens because AI doesn’t “understand” the way humans do. It relies on statistical regularities in the data, not intuition or reasoning.
- They still struggle with complex datasets
Complex environments, such as dynamic settings, often confuse AI models. This is because they are primarily trained on data sets with static images or objects. So when the environment changes constantly, or when objects overlap each other, the model might miss crucial details, leading to mistakes like misidentifying a person as a shadow or completely ignoring an object in motion.
What Happens with Humans-in-the-Loop
The benefits of human supervision in AI model training and handling edge cases are many, including:
- Enhanced visual data annotation quality: They bring more context, reasoning, and expertise into the data labeling process to ensure it accurately reflects real-world scenarios. For instance, annotators can classify an image of a car in an automated parking system not just as a “vehicle” but as a specific make and model, such as a compact hatchback versus a large SUV.
- Better resolution of ambiguities: When the data includes edge cases like objects in shadows or unusual lighting conditions, data annotators can make judgment calls on what the object is.
- Creation of layered annotations: Seasoned data annotators can provide detailed, multi-level labeling to help the computer vision model understand an object’s proper structure. Consider the example of a geographic data mapping system. Where AI identifies an area as a forest and maps its boundaries, data annotators can enhance this further (with as much as 40% more accuracy) by specifying individual elements within the forest, such as rivers, trails, clusters of specific tree types, and clearings.
Challenges in Implementing HITL for Edge Case Management in AI
Achieving the perfect balance in utilizing AI, as well as annotators’ oversight for handling computer vision edge cases, is a dream for many businesses. This is because implementing a Human-in-the-Loop approach in AI model training presents several challenges:
- Scalability Constraints: Human-guided edge case management is a resource-incentive approach. It requires seasoned data annotators to oversee the entire process, from data collection & annotation to model retraining and iteration. Moreover, as the volume of data and complexity of tasks increases, maintaining this consistency becomes challenging.
- Cost Concerns: Employing human resources adds to operational costs, including expenses related to hiring, training, compensation, and management of data annotation specialists. Additionally, even if you employ some tool/software, there will be a hefty fee involved.
- Latency: Including humans in a largely automated workflow can introduce some delays because:
- Manual batch processing takes time.
- You cannot predict data annotators’ availability.
- Human involvement comes with discussions and feedback.
- Human-labeled data often undergoes additional checks, such as peer reviews or supervisor validation.
- The complexity of edge cases can impact how long a human takes to interpret and annotate them.
Addressing HITL Challenges with Expert Data Annotation Services
Combining HITL with edge case management in AI model training does not have to be difficult, especially with professional data labeling and annotation services. These service providers have established workflows and a team of highly vetted data annotators skilled in dealing with 2D/3D images, videos, and other visual data. They understand the subtle nuances of potential edge cases in AI models.
To Overcome Scalability Constraints
Professional service providers have a vast pool of data annotators. This gives you the flexibility to scale up or down as needed. You can augment your teams if you need more hands on deck and resize when things are covered.
To Solve Cost Concerns
Partnering with a data annotation service provider gives you the option of choosing from multiple engagement options. You can hire dedicated data annotators for ongoing projects, work with an assembled team, or engage professionals on a time & scope basis. This allows you to customize your approach without stretching your dollar.
To Avoid Operational Latency
When you outsource data annotation to an external service provider, you get access to highly trained and professional annotators who can easily catch onto your project’s progress. Additionally, they have established and automated workflows for everything—data collection, annotation, validation, and QA. This reduces latency as most things are automated but with proper human oversight.
After all, edge case management in AI model training is no longer just a technical necessity; it is the only way to build consumer trust in AI systems. And involving humans in this loop is a foolproof way to achieve this!
