OpenHUTB/nn
Apr 2026 – Apr 2026
1 Month active
Languages Used
BashBatchBatchfilePowerShellPython
Technical Skills
AI DevelopmentComputer VisionData AnalysisData VisualizationDeep LearningGit
iiiostar
PROFILE
Iiiostar
Developed an end-to-end vehicle counting and analytics system for the OpenHUTB/nn repository, integrating YOLO-based detection with Carla simulation to deliver real-time traffic insights. Focused on expanding detection coverage through multi-scale region-of-interest and input scaling, the work enabled accurate distant-vehicle recognition and dynamic analytics such as runtime confidence adjustment and vehicle speed estimation. Enhanced data visualization panels and classification statistics provided actionable monitoring and model evaluation. Addressed repository hygiene by refining Python virtual environment management and .gitignore configuration, improving CI/CD reliability. Leveraged Python, deep learning, and computer vision expertise to lay a scalable foundation for future analytics and experimentation.
Overall Statistics
Feature vs Bugs
90%Features
Repository Contributions
23Total
Bugs
1
Commits
23
Features
9
Lines of code
10,311
Activity Months1
Your Network
263 peopleWork History
April 2026
23 Commits • 9 Features
Apr 1, 2026April 2026 (OpenHUTB/nn) Summary: Delivered end-to-end YOLO-based vehicle counting and analytics with Carla integration, expanding detection coverage and enabling real-time metrics that drive traffic insight and product decisions. The month focused on delivering key features, stabilizing the development environment, and laying the foundation for scalable analytics and experimentation. Key features and capabilities delivered: Carla connection diagnostics configured and YOLO11n vehicle counting successfully executed; distant-vehicle detection improved via multi-scale ROI and YOLO input scaling to 1280; real-time analytics including runtime confidence threshold adjustment and vehicle speed estimation; enhanced visualization and statistics panels for monitoring; baseline and YOLO-powered vehicle classification statistics; and vehicle trajectory length statistics with real-time distribution visualization. Repository hygiene and environment stability were addressed to improve developer experience and CI/CD reliability (comprehensive removal of Python virtual environment and fixes to .gitignore across multiple commits).
23 Commits • 9 Features
Apr 1, 2026April 2026 (OpenHUTB/nn) Summary: Delivered end-to-end YOLO-based vehicle counting and analytics with Carla integration, expanding detection coverage and enabling real-time metrics that drive traffic insight and product decisions. The month focused on delivering key features, stabilizing the development environment, and laying the foundation for scalable analytics and experimentation. Key features and capabilities delivered: Carla connection diagnostics configured and YOLO11n vehicle counting successfully executed; distant-vehicle detection improved via multi-scale ROI and YOLO input scaling to 1280; real-time analytics including runtime confidence threshold adjustment and vehicle speed estimation; enhanced visualization and statistics panels for monitoring; baseline and YOLO-powered vehicle classification statistics; and vehicle trajectory length statistics with real-time distribution visualization. Repository hygiene and environment stability were addressed to improve developer experience and CI/CD reliability (comprehensive removal of Python virtual environment and fixes to .gitignore across multiple commits).
April 2026
Activity
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Quality Metrics
Correctness94.0%
Maintainability91.4%
Architecture91.4%
Performance91.4%
AI Usage31.4%
Skills & Technologies
Programming Languages
BashBatchBatchfilePowerShellPython
Technical Skills
AI DevelopmentComputer VisionData AnalysisData VisualizationDeep LearningGitMachine LearningPythonPython DevelopmentPython developmentRoboticsScriptingSoftware EngineeringVersion controlVirtual Environment Management
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