AllML
PDSA - Pedestrian Detection Sensitivity Analysis
PDSA is a comprehensive machine learning project focused on analyzing and improving pedestrian detection systems for autonomous vehicles and smart city applications. The project involves developing robust detection algorithms and conducting sensitivity analysis to understand how various factors affect detection accuracy.
Duration
3 months
Team Size
Solo project
Role
ML Engineer & Computer Vision Specialist
Technologies Used
PythonOpenCVYOLOTensorFlowPyTorchComputer VisionDeep Learning
Key Features
- Real-time pedestrian detection
- Sensitivity analysis tools
- Performance metrics dashboard
- Multi-camera support
- Weather condition adaptation
- Edge case detection
Challenges
- Handling various lighting conditions
- Detecting partially occluded pedestrians
- Ensuring real-time performance
- Adapting to different weather conditions
Solutions
- Implemented multi-scale detection
- Used data augmentation for edge cases
- Optimized model architecture
- Applied transfer learning techniques
Results & Achievements
Achieved 94% detection accuracy
Reduced false positives by 30%
Improved performance in low-light conditions
Successfully deployed in test environment