SAR Vision processes drone video in real time and flags potential human contacts for operator review. It is designed for field conditions: no network required, no proprietary hardware, no cloud dependency. Built to support the judgment of trained SAR personnel — not to replace it.
Standard detection models are trained on ground-level photography from consumer and autonomous vehicle datasets. Aerial SAR presents a different visual domain — and the cost of a missed detection is not a degraded performance metric. It is a person not found.
General models are trained on upright figures at ground level. Aerial footage shows overhead silhouettes, foreshortened limbs, and partial figures. These models were not trained to recognize subjects from above.
Subjects in distress are frequently stationary, wearing earth-tone clothing, and partially obscured by terrain or vegetation. Without specific aerial SAR training data, detection systems do not generalize to these conditions reliably.
Cloud-based inference depends on uplink bandwidth that does not exist across most active search areas. Any system requiring network connectivity is unsuitable for remote field deployment.
CPU-only inference cannot maintain real-time processing of 1080p drone video on standard field equipment. Processing gaps mean a subject may be in frame during an interval that was never evaluated.
"A false positive costs an investigation team minutes. A false negative may cost the subject their life."
Designed to prioritize recall in search-and-rescue operational contexts. This tuning strategy intentionally surfaces low-confidence detections for operator review rather than suppressing them. SAR operations invert this priority completely. Verification of a false positive is a recoverable outcome. Suppression of a valid detection is not.
SAR Vision is configured to prioritize recall over precision: all plausible contacts are surfaced for operator review, including low-confidence candidates. This is the correct engineering decision for this operational context.
DETECTION METRIC TRADE-OFF
Team investigates flagged area. No subject found. Cost: bounded time for investigation.
Subject is in frame. System does not flag. Team continues past. Cost: potentially mission-critical.
Illustrative comparison based on internal evaluation datasets. Not independently validated.
Standard detection benchmarks weight precision and recall equally, or optimize toward precision because false positives carry a higher perceived cost. In SAR operations, that optimization is incorrect. SAR Vision is configured to surface all contacts the model considers plausible, including low-confidence candidates.
Confidence thresholds are configurable, but defaults are intentionally conservative. A detection at 0.45 confidence is presented to the operator for assessment rather than suppressed before review. The system aims to improve the likelihood that plausible contacts are not filtered before a human evaluates them.
This approach is reflected throughout: in training data selection, threshold configuration, and NMS parameter tuning — all oriented toward SAR context rather than general benchmark performance.
SAR Vision surfaces contacts. It does not adjudicate them. Every flagged detection requires human verification before any operational action. The system provides decision support; the operator retains decision authority.
A single-purpose detection tool: process drone video and surface human contacts to the operator in real time. Deployable on existing field hardware. No additional infrastructure required.
Detection model fine-tuned on annotated aerial imagery representative of search-and-rescue operational contexts — not repurposed from surveillance or autonomous vehicle training data. Trained to identify subjects from overhead perspectives in challenging terrain conditions.
Accepts HDMI capture from any drone monitor output, RTMP stream endpoints from mission planning software, or pre-recorded video files for post-flight review. No proprietary SDK required.
Runs on-device using CUDA-capable GPU hardware. A mid-range field laptop with an RTX 3060 is sufficient for real-time 1080p inference. All processing is local — no data leaves the device.
Confidence thresholds and post-processing parameters are configured to maximize detection coverage over precision. Low-confidence contacts are presented for operator review rather than suppressed at threshold.
All flagged contacts require human confirmation. SAR Vision does not take autonomous action or determine subject status. It presents contacts for assessment; the operator decides what action, if any, to take.
Does not replace SARTopo, CalTopo, or existing incident command tools. Adds a systematic aerial detection layer to drone operations teams are already conducting, without modifying established procedures.
Accepts HDMI capture (any USB capture card), RTMP endpoints from FPV or mission software, or local video files. Frame extraction is decoupled from inference to prevent throughput bottlenecks.
Frames normalized to model input dimensions. Optional adaptive contrast enhancement available for flat-lighting conditions common in overcast aerial environments.
Custom weights derived from YOLO base architecture, fine-tuned on annotated aerial imagery representative of search-and-rescue operational contexts. Training includes overhead perspective, partial occlusion, varied terrain, and low-contrast subject presentations. GPU-accelerated via CUDA. Not standard COCO weights.
NMS applied with SAR-tuned parameters. Contacts above configurable thresholds annotated on live feed. All events logged with timestamp and frame reference for post-mission review.
Designed as standalone module and as a detection input within the SARCommand incident management concept, currently under development.
SAR operations don't occur in controlled environments. SAR Vision is designed to function under the constraints that actually exist during active search deployments.
All inference runs locally. Model weights are bundled with the application. No license server, no cloud API, no outbound data. Operational in dead zones, canyon terrain, and remote wilderness.
Designed to run on equipment the team already carries. No specialized hardware beyond a CUDA-capable GPU. Packaging targets straightforward installation on existing field laptops.
Compatible with any drone providing a video output — DJI, Autel, Skydio, or other platforms via HDMI capture or RTMP stream. No manufacturer-specific integration required.
Recorded flight video can be processed after landing. Useful when operational conditions require full operator attention during flight, or for documentation and after-action review.
SAR Vision is an additional detection layer for drone-equipped operations. It does not replace SARTopo, incident command structure, or field coordinator judgment. Teams continue operating with existing tools; SAR Vision provides systematic aerial coverage that cannot be maintained manually at scale.
SAR Vision is in active operational evaluation. The following reflects the current testing state. No performance claims are made beyond what has been directly observed and documented.
Demonstrated to regional SAR personnel. The system was reviewed and observed by active search-and-rescue team members under structured conditions. Operational feedback from those sessions is incorporated into the development cycle.
Tested against live drone HDMI feeds. SAR Vision has been validated against real-time drone video via HDMI capture, confirming inference pipeline stability and frame throughput under field-representative conditions.
Operated under field deployment conditions. System has been run on portable field hardware, confirming offline functionality, GPU acceleration on laptop-class equipment, and detection output during active UAV flight.
Aerial SAR training data applied. Model weights reflect fine-tuning on annotated aerial imagery, demonstrating improved detection of overhead human figures compared to the untuned base model baseline.
Structured agency evaluation in planning. Formal evaluation with drone-equipped SAR units is being organized for the next development phase. Participating units will contribute structured operational feedback.
Human verification is required for all detections. No output from SAR Vision should be acted upon without evaluation by a qualified operator. The system surfaces candidates; personnel assess them.
Performance varies by terrain, lighting, and subject visibility. Detection reliability is affected by vegetation density, terrain complexity, ambient light conditions, and subject contrast against background. No system performs uniformly across all environments.
False positives are expected and by design. The recall-optimized configuration intentionally accepts a higher false positive rate. Teams should plan for flagged contacts that do not correspond to subjects on every deployment.
CUDA-capable GPU is a deployment prerequisite. CPU-only hardware is not recommended for real-time deployment. This requirement must be confirmed before evaluation planning begins.
Active development build. SAR Vision is under continuous development. Evaluation units should expect iterative updates and are expected to provide structured operational feedback as part of participation.
Representative detection scenarios illustrating the types of contacts SAR Vision surfaces, including the reasoning behind each flag. Annotated screenshots from active field evaluation will replace these placeholders as testing progresses.
Example frames shown are representative field test imagery. Performance varies based on terrain, lighting, altitude, and subject visibility.
SAR Vision was built for a specific operational context. Understanding where it fits — and where it does not — is part of evaluating whether it is appropriate for your unit.
Units conducting UAS-assisted searches who need systematic coverage of aerial video beyond what manual operator review can sustain.
Teams whose search areas lack reliable cellular or satellite uplink — remote wilderness, canyon terrain, or backcountry without communications infrastructure.
Personnel who want to increase detection coverage without delegating subject identification to the system — operators remain in the loop on every flagged contact.
Government and accredited volunteer units with structured operational procedures who can integrate detection assistance into existing field workflows.
SAR Vision requires active operator oversight. It is not architected for unattended or autonomous drone patrol without human monitoring.
Designed for organized SAR teams with formal operational structures and trained personnel. Not intended for personal or recreational aerial use.
SAR Vision runs locally and does not transmit data externally. Teams requiring centralized remote processing infrastructure should evaluate other solutions.
The system does not provide autonomous GPS coordinates, subject tagging, or automatic dispatch triggers. All detections require human review and interpretation before any action.
All detections require human verification. No detection output from SAR Vision should be acted upon without evaluation by a qualified operator. The system surfaces candidates; trained personnel assess them.
SAR Vision does not replace operator judgment. Aerial observation, search pattern planning, and subject determination remain under the authority of qualified SAR personnel. The system is an analytical aid, not a decision authority.
It is not an autonomous search system. SAR Vision does not direct aircraft, prioritize search sectors, or make resource allocation decisions. These functions remain entirely with incident command and field personnel.
Detection rates are environment-dependent. No threshold guarantees that all subjects present in video will be flagged. System performance should be understood as probabilistic assistance, not exhaustive coverage.
SAR Vision is under active development. The current build addresses human detection from RGB aerial video. Subsequent phases are planned based on operational priority and feedback from evaluation units.
Roadmap priorities are shaped directly by feedback from evaluation units. If your operational context involves specific terrain types, detection challenges, or equipment constraints not addressed here, that input is sought and directly informs development sequencing.
SAR Vision is currently undergoing structured field evaluation with select SAR teams. Participation is limited while the system continues refinement, operational validation, and training dataset expansion.
This is a collaborative development phase. Participating units are expected to contribute observations on detection performance, false positive rates, field usability, and integration with existing procedures. That feedback directly determines development priorities.
Participation is coordinated directly with evaluation units, including structured check-ins, deployment guidance, and feedback review after operational use.
Field evaluation participation — active SAR units only