Computer Vision Development & Hardware Integration
Engineering at the intersection of software and the physical world: computer vision, edge AI, and embedded hardware working together in robotics, drones, and smart camera systems. Built on decades of applied CV research: from algorithm design through hardware integration to production deployment.
What We Propose? All around computer vision
Classical computer vision: image normalization (rotation, scale, and translation invariance via moment and integral-transform methods), edge detection, feature extraction (SIFT, ORB), when these outperform deep learning on small datasets or hard real-time budgets
Deep-learning vision: object detection (YOLO-family, Faster R-CNN), semantic and instance segmentation (U-Net, Mask R-CNN), pose estimation, tracking across frames
Hybrid pipelines that combine classical pre-processing with a learned model downstream: often faster and more data-efficient than an end-to-end network alone
Edge AI optimization: quantization, pruning, and conversion to TensorRT / ONNX Runtime for deployment on constrained hardware
Hardware integration: camera selection (global vs. rolling shutter, spectral range), embedded platforms (NVIDIA Jetson, Raspberry Pi, custom PCB), sensor fusion with LiDAR/IMU where a single camera isn't enough
Control-loop integration: closing the loop from a vision estimate directly to an gripper or servos commands
How We Decide How to Build
The method is chosen for the task, not by default to "just use a neural network"
A rotation- and scale-invariant classical normalizer, built on angular-kernel or moment-invariant methods, can outperform a deep network when the dataset is small, the problem geometry is well understood, or a tight real-time budget on constrained hardware rules out a large model. Deep learning is used where it's genuinely warranted: high appearance variability, large labeled datasets, or open-set recognition that a hand-crafted feature can't cover.
Every recommended method states its own limits
A model recommendation always comes with the conditions under which it's expected to hold: lighting range, occlusion tolerance, viewing-angle range, and what happens outside them. A system that quietly degrades outside its validated envelope is a liability. Ours is explicitly scoped so we know failure modes in advance rather than discovering them in the field.
Edge deployment is designed in from the start, not bolted on after
If the target is a Jetson or a custom embedded board, the model architecture, quantization strategy, and inference framework are chosen from the first prototype, keeping that hardware's memory and compute budget in mind, rather than training a large model in the cloud only to discover it doesn't fit.
Vision output feeds a control law directly, when the application calls for it
For robotics and drone applications, we design the path from pixel displacement to actuator command as a single derivation; with the right formulation, scale ambiguity in the image cancels algebraically from the control law, eliminating the need for a separate distance sensor. This is control-theory-grade engineering, not a vision system and a controller stapled together after the fact.
The method is chosen for the task, not by default to "just use a neural network"
A rotation- and scale-invariant classical normalizer, built on angular-kernel or moment-invariant methods, can outperform a deep network when the dataset is small, the problem geometry is well understood, or a tight real-time budget on constrained hardware rules out a large model. Deep learning is used where it's genuinely warranted: high appearance variability, large labeled datasets, or open-set recognition that a hand-crafted feature can't cover.
Every recommended method states its own limits
A model recommendation always comes with the conditions under which it's expected to hold: lighting range, occlusion tolerance, viewing-angle range, and what happens outside them. A system that quietly degrades outside its validated envelope is a liability. Ours is explicitly scoped so we know failure modes in advance rather than discovering them in the field.
Edge deployment is designed in from the start, not bolted on after
If the target is a Jetson or a custom embedded board, the model architecture, quantization strategy, and inference framework are chosen from the first prototype, keeping that hardware's memory and compute budget in mind, rather than training a large model in the cloud only to discover it doesn't fit.
Vision output feeds a control law directly, when the application calls for it
For robotics and drone applications, we design the path from pixel displacement to actuator command as a single derivation; with the right formulation, scale ambiguity in the image cancels algebraically from the control law, eliminating the need for a separate distance sensor. This is control-theory-grade engineering, not a vision system and a controller stapled together after the fact.
Classical CV
OpenCV, custom normalization/moment-invariant pipelines, scikit-image
Deep Learning
PyTorch, YOLO-family detectors, Mask R-CNN / U-Net segmentation, Ultralytics tooling
Data & Model Lifecycle
CVAT / Label Studio for annotation, DVC for data and model versioning, MLflow / Weights & Biases for experiment tracking, CI/CD for model releases
Edge Optimization
TensorRT, ONNX Runtime, quantization-aware training, model pruning
Embedded Hardware
NVIDIA Jetson (Orin/Xavier), Raspberry Pi + Coral accelerator, custom PCB integration where required
Sensors
Multispectral cameras, LiDAR, IMU fusion, robotics and drone sensor rigs
Real-Time Control
Real-time control loops (ROS2-based where applicable), sensor-to-actuator latency budgeting
Monitoring & Traceability
OpenTelemetry, Grafana/Prometheus, structured logging with correlation IDs traced back to the originating user story
