
Fernando Crespo developed core perception and front-end features across robotics and web projects, focusing on Autonomous-droneProject/Kestrel and KnightHacks/forge. For Kestrel, he built a real-time vision node integrating YOLOv8 and DeepSORT with ROS, enabling robust object detection and multi-object tracking from live camera feeds, and established a modular pipeline for detection, embedding extraction, and ROS message publishing using Python and PyTorch. On KnightHacks/forge, he enhanced user experience by adding page-level metadata for SEO and extending flowchart commands to support multiple catalog years, leveraging TypeScript and React. His work demonstrated depth in computer vision, robotics integration, and full stack development.
February 2026 monthly summary for KnightHacks/forge. Focused on delivering user-visible improvements and year-aware tooling that align with business goals around discoverability and planning across catalog years. No major bugs fixed this month.
February 2026 monthly summary for KnightHacks/forge. Focused on delivering user-visible improvements and year-aware tooling that align with business goals around discoverability and planning across catalog years. No major bugs fixed this month.
August 2025 monthly summary for Autonomous-droneProject/Kestrel: Delivered the Kestrel Vision Node enabling real-time person detection with YOLO, CNN embeddings, and ROS integration. Established a robust data flow: image input via ROS, detection outputs, and embeddings published. Implemented debugging visualization to validate performance. Created a modular processing helper to streamline detection and embedding extraction. This work lays the foundation for downstream analytics, autonomous navigation, and human-robot interaction, with a clear, traceable commit history.
August 2025 monthly summary for Autonomous-droneProject/Kestrel: Delivered the Kestrel Vision Node enabling real-time person detection with YOLO, CNN embeddings, and ROS integration. Established a robust data flow: image input via ROS, detection outputs, and embeddings published. Implemented debugging visualization to validate performance. Created a modular processing helper to streamline detection and embedding extraction. This work lays the foundation for downstream analytics, autonomous navigation, and human-robot interaction, with a clear, traceable commit history.
Monthly summary for 2025-07 focusing on delivering core perception capabilities for Autonomous-droneProject/Kestrel. Key features include a Real-time Vision Node with YOLOv8 integration that publishes detections and debug frames over ROS at 30 Hz (capturing video from the default camera), and a DeepSORT integration for ROS with a dataset and training workflow to support robust multi-object tracking. No major bugs reported this month. These efforts advance real-time perception, situational awareness, and autonomous operation, enabling safer flights and easier integration with ROS-based workflows. Technologies demonstrated include YOLOv8, ROS, PyTorch, DeepSORT, a Market-1501 dataset, CNN feature extraction, and data association modules.
Monthly summary for 2025-07 focusing on delivering core perception capabilities for Autonomous-droneProject/Kestrel. Key features include a Real-time Vision Node with YOLOv8 integration that publishes detections and debug frames over ROS at 30 Hz (capturing video from the default camera), and a DeepSORT integration for ROS with a dataset and training workflow to support robust multi-object tracking. No major bugs reported this month. These efforts advance real-time perception, situational awareness, and autonomous operation, enabling safer flights and easier integration with ROS-based workflows. Technologies demonstrated include YOLOv8, ROS, PyTorch, DeepSORT, a Market-1501 dataset, CNN feature extraction, and data association modules.

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