Computer VisionObject DetectionPyTorchCUDA
Terrain Vision AI
A computer vision system for real-time terrain analysis, obstacle detection, and path planning from aerial and ground-level imagery.
This project is currently in active development.
The Problem
Autonomous systems operating in unstructured terrain need real-time environmental understanding from standard camera feeds — without expensive LIDAR or GPS infrastructure that is impractical in the field.
The Solution
A computer vision pipeline using PyTorch and CUDA acceleration for real-time terrain analysis, obstacle detection, and path planning from both aerial and ground-level imagery with low-latency edge inference.
Architecture
01Multi-source image capture and preprocessing pipeline
02Custom object detection model trained on terrain datasets
03Monocular and stereo depth estimation
04Real-time terrain classification and segmentation
05Path planning output layer for downstream autonomous systems
Results & Outcomes
Real-time inference at 30+ FPS on edge hardware
Accurate obstacle detection across diverse terrain types
Validated on both aerial and ground-level imagery datasets
CUDA-accelerated for low-latency field deployment