detections/_docs/00_problem/problem.md

Problem Statement

What is this system?

Azaion.Detections is an AI-powered object detection microservice designed for aerial reconnaissance. It processes drone and satellite imagery (both still images and video) to automatically identify and locate military and infrastructure objects — including armored vehicles, trucks, artillery, trenches, personnel, camouflage nets, buildings, and more.

What problem does it solve?

Manual analysis of aerial imagery is slow, error-prone, and does not scale. When monitoring large areas from drones or satellites, a human analyst cannot review every frame in real time. This service automates the detection process: given an image or video feed, it returns structured bounding boxes with object classifications and confidence scores, enabling rapid situational awareness.

Who are the users?

• Client applications that submit media for analysis (via HTTP API)
• Downstream services (Annotations service) that store and present detection results
• Real-time consumers that subscribe to Server-Sent Events for live detection updates during video processing

How does it work at a high level?

1. A client sends an image or triggers detection on media files available in the Loader service
2. The service preprocesses frames — resizing, normalizing, and for large aerial images, splitting into GSD-based tiles to preserve small object detail
3. Frames are batched and run through a YOLO-based object detection model via TensorRT (GPU) or ONNX Runtime (CPU fallback)
4. Raw model output is postprocessed: coordinate normalization, confidence thresholding, overlapping detection removal, physical size filtering, and tile deduplication
5. Results are returned as structured DTOs (bounding box center, dimensions, class label, confidence)
6. For video/batch processing, results are streamed in real-time via SSE and optionally posted to an external Annotations service

Domain context

The system operates in a military/defense aerial reconnaissance context. The 19 object classes (ArmorVehicle, Truck, Vehicle, Artillery, Shadow, Trenches, MilitaryMan, TyreTracks, AdditArmoredTank, Smoke, Plane, Moto, CamouflageNet, CamouflageBranches, Roof, Building, Caponier, Ammo, Protect.Struct) reflect objects of interest in ground surveillance. Three weather modes (Normal, Winter, Night) provide environment-specific detection variants. Physical size filtering using ground sampling distance ensures detections are physically plausible given camera altitude and optics.