The tracebloc Playbook: How to Achieve Top Performance in Drone Object Detection

Tracebloc is a tool for benchmarking AI models on private data. This Playbook breaks down how a team used tracebloc to benchmark AI models on their drone footage and discovered which model truly delivered the best results. Find out more on our website or schedule a call with the founder directly.

Why Model Performance Matters

Every inaccurate decision costs money and safety, but not every model holds up under stress condistions. Using tracebloc, a drone analytics company uncovered which UAV object detection model truly performs under pressure, saving over €3 million a year.

Step 1: The Challenge 

Following several large-scale public events and natural disasters, city authorities are turning to drone crowd monitoring for better eyes in the sky. Juliane Weber, Head of Operations at a UAV image analysis company, is developing an AI-powered drone computer vision platform for drone traffic monitoring and drone crowd monitoring. The goal: detect people, vehicles, and emergency units instantly, even in smoke, low light, or dense crowds.

Key Requirements

• 90 % precision (IoU ≥ 0.5) across 11 object classes, including rare ones like wheelchairs or fire trucks. The UAV machine learning system measures frequency-weighted recall across classes.
• Real-time inference < 20 ms per frame on NVIDIA Jetson Orin enabling real time UAV object detection
• Robust UAV traffic monitoring under emergency conditions (e.g. smoke, low light, high density)

With a dataset of 7 000 aerial images and 350 000 annotations, her team decided to benchmark external UAV deep learning vendors to find which model actually performed best.

Step 2: What the Vendors Claimed

Each vendor submitted drone object detection models, optimized for deployment on embedded drone hardware (e.g. NVIDIA Jetson Orin NX).

Vendors were asked to state overall object detection performance as well as per-class F1 scores for rare object classes (e.g. wheelchair user, police car, fire truck). Robustness under occlusion and crowd density was emphasized, as was edge inference latency under 20 ms.

While all vendors claimed high recall on common object classes (e.g. car, pedestrian, person), Juliane’s team focused their assessment on:

• Rare class average recall
• Occlusion handling in dense crowds or tight urban spaces
• Model robustness in suboptimal weather or smoke conditions

Step 3: Secure Evaluation and Fine-Tuning

Using tracebloc, she set up an evaluation environment on isolated edge AI hardware. Vendors never saw the raw data yet could run their object detection models directly on real aerial footage from the company’s test set of 2 000 annotated images. In the next phase, they fine-tuned their models on an additional 5 000 training images and re-evaluated performance.

Step 4: Observed Results After Testing

After fine-tuning, performance varied significantly especially on rare object classes. Recall was measured at 90% precision with IoU ≥ 0,50:

Vendor B`s RT-DETR transformer model delivered the most balanced performance across common and rare object classes, with the second highest overall recall post-fine-tuning and rare class F1 above 80%. Others struggled to close the gap on infrequent objects. Vendor D’s DINOv2 model neglected rare object classes to boost overall baseline recall and hence was not considered further. All vendors met latency requirements.

Step 5: Business Case – Smarter Drones Save Real Money

Every percentage point of improved detection reduces chaos on the ground. Drone reconaissance helps command units respond faster and avoid costly mistakes on the ground.

Scenario Assumptions:

• 1.000 events or crisis situations per year with an average of 100 personnel decisions taken per event, i.e. 100.000 decisions per year
• Each missed object triggers a resource misallocation through false or delayed decisions, in severe cases this can cause event cancellations or safety risks
• Juliane estimates the average cost of a misallocation of resources at about €1.000, misallocation rate equals the share of missed objects after fine tuning

Estimated annual cost of misallocations based on overall recall at 90% precision and IoU ≥ 0,50:

Step 6: Decision – Drone Object Detection with Vendor B

Vendor B offers the best trade-off between a high recall 94,5% and strong rare object detection at F1>80,6%. The saving potential is €3,2m p.a. compared to the next best model, highlighting the importance of strong model performance for drone reconnaissance.

Next Steps:

• Gradual deployment UAV traffic monitoring across major cities
• Continuous data collection for further fine-tuning

Disclaimer: 
The persona, figures, performance metrics, and financial assumptions in this case study are fictional and simplified to reflect realistic industry logic. This case is designed to illustrate AI benchmarking and does not reflect actual vendor performance or contractual outcomes.

Drone Object Detection FAQs

What are the main challenges?

• Maintaining high detection accuracy under variable conditions such as low light, crowd density, and occlusion, while keeping real-time performance on limited edge hardware
• Keeping data private while evaluating external model´s performance
• Finding the best drone traffic monitoring model that meets the optimum between performance, weight, latency and cost.

Which model performs best for UAV computer vision?

The RT-DETR model delivers the best balance of latency, precision, and cost by combining transformer accuracy with real-time efficiency optimized for embedded drone hardware.

What is tracebloc?

tracebloc is a tool for benchmarking third party AI models on your own proprietary data. Find out more on the website or schedule a call with us directly. Click "Join use case" if you would like to try it yourself and explore the docs for technical details.