Retail Company Uses

MLtwist for Safe and Accurate Drone Delivery Vision

The Use Case

As retailers explore drone delivery to speed up last mile logistics, computer vision becomes critical to ensuring safety and precision. A large retail company partnered with MLtwist to build a computer vision training dataset that would allow delivery drones to safely navigate residential backyards while identifying people, pets, and surrounding objects before package dropoff.

The model required dense, frame level annotations across drone video footage, with each frame taking roughly 10 minutes to label properly. The footage itself was highly unstable due to wind driven movement, with drones shifting left and right and moving up and down throughout each flight, significantly increasing annotation complexity.

THE CHALLENGE

Turning Unstable Drone Footage into Safety Critical Training Data

The project introduced several key challenges:

• Unstable Video Quality: Wind and drone movement caused constant changes in camera angle and perspective, requiring continuous adjustment of annotations on every frame.
• High Precision Requirements: The model needed to accurately detect humans, pets, and backyard objects to prevent injury or property damage during delivery.
• Slow Frame Level Labeling: At 10 minutes per frame, productivity and consistency were at risk without workflow optimization.
• Complex Scene Variation: Each backyard introduced new layouts, lighting conditions, and object combinations, increasing the risk of missed detections or annotation drift.

MLtwist’s Approach

Tool Selection and AI Assisted Pre Labeling

MLtwist evaluated and selected the most effective labeling tools for high motion drone video. AI assisted pre labeling was customized to handle unstable footage, giving annotators a strong starting point while minimizing correction time caused by camera movement and blur.

Advanced Annotation Techniques for Motion Heavy Video

Annotators used specialized video annotation workflows designed for dynamic scenes. MLtwist applied proprietary consistency checks to maintain accurate object tracking across frames, even as drones shifted position due to wind.

Safety Focused Annotation Taxonomy

MLtwist designed a clear and structured labeling schema that prioritized safety critical classes such as humans, pets, and fragile backyard objects. This ensured the training data directly supported safe decision making during drone delivery.

Multi Layer Quality Assurance

Given the time intensive nature of frame level labeling, MLtwist implemented a multi stage QA process combining automated validation with expert human review. This approach ensured high recall on safety related objects while maintaining consistent annotation quality across large video volumes.

Impact & Benefits

• High Quality Annotations in Low Quality Video: MLtwist maintained accuracy despite motion blur and unstable drone footage.
• Improved Labeling Efficiency: Optimized tools and AI assistance reduced per frame correction time while preserving precision reducing the labeling time per image to 7 minutes which is a 30% reduction in time and cost .
• Safety Ready Training Data: Accurate detection of people, pets, and backyard elements supported safer drone delivery operations.
• Scalable Delivery: MLtwist delivered consistent, production ready datasets suitable for ongoing model training and iteration.

The Takeaway

By combining the right labeling tools, AI assisted workflows, and rigorous quality assurance, MLtwist transformed unstable drone footage into high quality computer vision training data. The result was a safety focused dataset that enabled a retail company to move closer to reliable and responsible drone delivery at scale.10