We build full AI pipelines for aerial applications: problem definition, collection strategy, dataset annotation, training, validation and deployment — onboard the drone (edge) or in the cloud (post-flight).
We work with modern detection architectures (YOLO, DETR), segmentation (SAM, Mask R-CNN) and specialized aerial-imagery models with geolocation. Every model is optimized for the target hardware — Jetson Orin, Coral, or server-side GPU inference.
We integrate the model with flight telemetry, producing georeferenced outputs: every detected object comes with latitude/longitude, timestamp and confidence metrics.
Understand the target object/event, the flight scenario and the KPIs (precision, recall, acceptable latency).
Directed data-collection flights, assisted annotation, augmentation and quality validation.
Fine-tuning of SOTA architectures on your dataset, hyperparameter ablation and cross-validation.
TensorRT/ONNX conversion, INT8 quantization and benchmarks on embedded hardware.
Fusion with GPS/IMU telemetry to produce detections with exact geographic coordinates.
Continuous re-training as new data arrives, drift monitoring and model versioning.
Plant counting, gap detection, pest identification and mapping of affected areas.
Automatic census, movement monitoring and identification of lost animals across large properties.
Defect identification on towers, transmission lines, roofs, solar panels — with automatic prioritization.
Perimeter and flow monitoring, vehicle counting, unauthorized-presence identification.
Automatic comparison across flights to measure construction progress, moved volumes and design compliance.
Deforestation detection, species counting, fire identification and mapping of degraded areas.
Models: YOLOv8/v11, DETR, RT-DETR for detection · SAM2 for segmentation · ConvNeXt and ViT for classification · DeepSORT/ByteTrack for tracking.
Training: PyTorch, MMDetection, Ultralytics · datasets versioned with DVC · experiment tracking with Weights & Biases.
Deployment: ONNX Runtime, TensorRT, OpenVINO for edge · NVIDIA Jetson Orin Nano/AGX, Google Coral · Python APIs for integration with post-flight pipelines.
Tell us what you want to detect, at what precision and where inference must run. We'll respond with a technical plan and quote.
