AI Survey Drones for Real-Time Crowd and Traffic Surveillance

Use case: Survey Drones with AI Detection for Crowd and Traffic Monitoring

Executive Summary:

Survey drones equipped with advanced object detection enable real-time crowd and traffic monitoring for safer, faster public response during major events. By using tracebloc to benchmark multiple vendors on recall, the company identified savings of over €3 million per year through smarter drone data analytics.

Step 1: The Use Case 

Following several large-scale public events and recent natural disasters, city authorities and event organizers are turning to AI survey drones for improved real-time situational awareness. Juliane Weber, Head of Operations at a drone analytics company, is developing an AI-powered survey drone platform that performs autonomous drone traffic monitoring and drone crowd monitoring from the air. These security drones are deployed at concerts, marathons, football matches, and during disaster response missions to map and analyze real-time human and vehicle movement for safer, smarter event management.

Key Requirements

• High detection quality measured in average recall at 90 % precision (IoU ≥ 0,5) across all 11 object classes, including partially occluded, rare, and small objects like people in wheelchairs, police cars, or fire trucks. The AI drone inspection system measures frequency-weighted recall across classes.
• Real-time inference directly on embedded edge AI hardware (e.g. NVIDIA Jetson Orin) with latency <20 ms per frame, enabling real time drone traffic and crowd monitorin
• Robust object detection under emergency conditions (e.g. smoke, low light, high density

Her team has access to a large, labeled dataset of ~7.000 aerial drone images with roughly 350.000 object annotations containing 11 object classes: pedestrian, person, bicycle, car, van, truck, bus, motorcycle and others.
Although her team has the technical ability and capacity, she decides to explore the drone image analysis market and find who could deliver the best performance or best overall drone inspection service.

Step 2: What the Vendors Claimed

Each vendor submitted proposals for 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, so that vendors could not access raw data, but evaluate their drone object detection models using real drone footage from the company’s test dataset consisting of 2.000 annotated aerial images. In a second step, the vendors were invited to fine-tune on the company’s training dataset consisting of 5.000 annotated images and evaluate their models again.
Metrics included overall recall, per-class F1 and latency.

Step 4: Observed Results After Testing

After secure fine-tuning, performance varied significantly across vendors, 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 Drone Data Analytics Saves Real Money

Every percentage point of improved detection reduces chaos on the ground. Survey drones help command units respond faster, allocate resources smarter, and avoid costly mistakes in crowd and traffic control.

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 – Full Drone Inspection Service 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 monitoring.

Next Steps:

• Gradual deployment of security drones and drone inspection services across major cities
• Continuous data collection for further fine-tuning
• Integration with real-time emergency dashboards

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.