Aerial Object Detector
Enhance airspace awareness and safety in your UAS operations
The Problem
Low-level airspace is increasingly congested with drones, helicopters, ultralights, and other flying objects.
Many aerial vehicles do not emit collaborative signals (e.g., ADS-B, Remote ID), making them invisible to traditional tracking systems.
Non-cooperative objects (e.g., balloons, birds, hobby drones) pose real collision risks to both manned and unmanned aircraft.
Situational awareness tools often fail to detect these silent actors, especially below 400 feet, where most UAS operations occur.
The result: increased risk of mid-air conflicts, mission disruption, and safety concerns in critical operations.
The Solution
AOD
Aerial Object Detection (AOD) is a solution for real-time detection, classification, tracking, and geolocation of flying objects from IR and/or EO video streams, whether onboard drones or from ground-based sensors.
AOD brings state of the art AI to airspace monitoring
Get video
Get any type of video stream, in any format, from any sensor on board a drone or on PTZ cameras on the ground
Analyze
Automatically analyze video in real time
Detect
Detect, classify, track and geo-locate relevant aerial targets
Get alerts
Generate actionable alerts to enhance situational awareness and prevent collisions
Deploy Anywhere
Deploy on board, locally or on the cloud
AOD Benefits
Collision Risk Reduction
Enhance Situational Awareness and prevent mid-air conflicts by detecting flying objects not visible to traditional systems
Real-Time Intelligence:
Process live video streams to provide immediate alerts, supporting faster and safer decision making during missions
Non-Cooperative Object Detection:
Identify objects that do not broadcast their position
Flexible Deployment:
Deploy onboard, in the cloud, or at ground control stations
Operational Continuity:
Consistent performance in dynamic environments , supporting day and night, IR and RGB imagery
Efficient Resource Use:
Focus on relevant aerial activity, to optimize bandwidth, storage and operator attention
AOD Specs
Input Sources and Format:
IP Cameras; video files; video streams (RTP, RTSP, RTMP), video platforms (Milicast, Dolby.io, DJI).
Output Video Format:
Dolby, WebRTC,
RTMP, RTSP,
HLS
Integration:
REST API,
Sense Web Front
Mavlink
Alarms:
JSON over web socket,
Webhooks,
Serial for onboard deployments
Supported Camera Types:
IP cameras,
USB and GbE cameras for onbard
Supported Targets:
UAV,
Birds,
Gliders
Training Database:
+200.000 images
Latency:
<1 s
Deployment:
Edge,
Cloud,
Private Cloud,
Local
AOD Metrics Of Merit
Detection Probability:
>90%
False Alarm Rate:
<10%
Detection Range Examples:
Small Drone: 500m
Small Cessna airplane: 3000m
Mission Insight Report
Summarize and enhance detection activity
for the full mission or selected timeframe
Interactive reports:
With the option to download as PDF
Offline and in-mission
Ideal for operators who prefer reviewing detections
over monitoring live video
Get first and last appearance
for each detected target
Includes geo-positioning data
for every detected track
Automatically display the best crop
of each target
Application Scenarios
For more information, Visit our youtube channel
API Documentation
For more information about cloud integration, visit our API Documentation or send us an email
