Challenges in Computer Vision Software

Key Issues and Challenges

Highlighting Limitations in Computer Vision Applications

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• Introduction to Vision Software
• Accuracy Limitations
• Bias and Fairness Issues
• Performance Challenges
• Privacy and Ethical Concerns
• Recommendations and Conclusion

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1

Introduction to Vision Software

What is Computer Vision and Its Common Applications

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• Computer vision software uses AI to interpret and understand visual data from images/videos
• Common applications: Autonomous vehicles, facial recognition, medical imaging, surveillance
• Powers everyday tech like photo tagging, object detection in apps

2

Accuracy Limitations

Challenges in Reliable Detection and Recognition

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• False positives/negatives common in varying conditions
• Error rates up to 20-30% in adverse weather/lighting
• Real-world example: Tesla Autopilot incidents due to misreads

• 34%: Object Detection Error
• 15%: Facial Recognition
• 22%: Pedestrian Detection
• 28%: License Plate Reading

3

Bias and Fairness Issues

Disparities in Model Performance Across Groups

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Light Skin Tones High accuracy: 99% match rate Trained on predominantly white datasets

Dark Skin Tones Low accuracy: 65% match rate Underrepresentation leads to failures

> Facial analysis technologies have higher error rates for certain demographics, exacerbating inequalities.

— NIST Study on Facial Recognition (2020)

4

Performance Challenges

Real-Time Processing and Resource Demands

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• High computational cost: Requires GPUs/TPUs for inference
• Latency issues: Delays in real-time apps like drones/autonomous cars
• Scalability limits: Struggles with high-resolution video streams
• Edge deployment challenges: Limited power on mobile/IoT devices

• Frames dropped due to computation overload
• Response time >100ms unacceptable for safety-critical apps
• Hardware bottlenecks limit deployment

5

Privacy and Ethical Concerns

Data Handling and Surveillance Risks

• Massive data collection without consent
• Surveillance risks in public spaces
• Data breaches expose biometric info
• Lack of transparency in black-box models

6

Recommendations for Improvement

Strategies to Address Key Problems

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🔍 Diverse Datasets Include varied demographics, lighting, angles

⚙️ Robust Testing Edge cases and adversarial examples

💻 Efficient Models Model optimization, quantization

🔒 Privacy-First Design Federated learning, anonymization

📊 Transparency Tools Explainable AI techniques

Vision software has transformative potential but faces significant hurdles in accuracy, bias, performance, and privacy. Prioritize robust development to unlock safe, ethical applications.

Thank you! Questions?

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