The Essential Guide to Applying AI to the Drone Delivery Ecosystem

No element is more essential in the drone delivery ecosystem than high-quality training data, which is used to develop machine learning models that update and refine rules for a drone's mission as it operates. Fortunately, drones can collect large quantities of data from a multitude of sensors, scanners, and cameras during the course of normal operations.

For drone mission planning, quality training data generates thousands of gameplays where programmers either reward or penalize a model for the actions it performs. The quality of this data has profound implications for the model’s subsequent development, setting a powerful precedent for all future applications that use the same training data.

The Challenge: AI and Computer Vision Data Quality in Drones

If you’re part of a drone project team, the success of your project is directly tied to the volume of high-quality data you receive. Enormous amounts of data are required to keep your drones traveling from point A to point B, error, and accident-free.

Additionally, because data comes in from various sensors and cameras, labeling data to ensure a safe model is highly technical. It’s also incredibly daunting because of the resources and time involved in capturing, preparing, and labeling data. Sometimes, teams compromise on the quantity or quality of data – a choice that will undoubtedly lead to significant problems down the line.

Drone Data: Pre-processing, Live-processing, and Post-processing

There are only 24 hours in a day, and your drone team should consider the amount of time needed for pre-processing, live processing, and post-processing data. While it might not be glamorous work, it needs to get done, and details matter. That’s why finding a partner who can work through repetitive tasks with precision and communication is critical.

Pre-processing drone data

Pre-processing drone data can create exact replicas of real-world items such as machines, rooftops, or automobiles. When using drones for inspection purposes, a combination of 2-D images, lidar sensors, and photogrammetry can be applied to create 3-D renderings of the space, a process commonly referred to as digital twin creation.

Digital twins create a bridge between the physical and digital worlds. For example, in industries like drone delivery, digital twins can train one pilot to operate one drone and one pilot to operate many drones before moving on to a drone dispatch model where there is no pilot. Performing these types of experimental training programs in the digital world has lower risk at a lower cost than in the physical world.

Live-processing drone data

Live-processing enhances object detection, helping the drone to make game-time decisions such as avoiding other aircraft, flying around weather, avoiding structures, and the ability to make emergency landings. For example, a lake of oil is not safe, but a lake of water is. They may look similar if the drone uses simple sensors, but computer vision differentiates the lakes and adds infinite value to operations.

Post-processing drone data

Post-processing drone data can considerably improve drone delivery safety and efficiency. For example, sensor, image, and video data can indicate the level of weather distress a drone may experience during flight. Suppose data suggests that high winds during a particular season affect battery life or performance. In that case, operators can adjust the route or make other alterations to the flight plan. Post-data processing can also examine if pre-determined flight paths are optimal based on demand. If not, flight paths can be adjusted accordingly.

Drone companies can transform their data operations to produce quality training data with the right combination of people, processes, and technology. To do it requires seamless coordination between an expert human workforce, your machine learning project team, and your labeling tools.

People: Choosing the right people to annotate or label your AI data is one of the most important decisions you’ll make. Computer vision and sensor learning models are only as good as the data they are trained on, which means you will need people, often referred to as “humans in the loop,” to prepare and quality-check your image, video, and sensor annotations.

Process: Designing a computer vision model for drones is an iterative process. Data annotation evolves as you train, validate, and test your models. Along the way, you’ll learn from their outcomes, so you’ll need to prepare new datasets to improve your algorithm’s results.

Your data annotation team should be agile. That is, they must be able to incorporate changes in your annotation rules and data features. They also should be able to adjust their work as data volume, task complexity, and task duration change over time.

Your team of data annotators can provide valuable insights about data features - that is, the properties, characteristics, or classifications you want to analyze for patterns that will help train the machine to predict the outcome you’re targeting.

Technology: Data annotation tools are software solutions that can be cloud-based, on-premise, or containerized. You can use commercial tools to annotate pipelines of production-grade training data for machine learning or take a do-it-yourself approach and build your own tool. Alternatively, you can choose one of the many data annotation tools available via open source or freeware.

The challenge is deciding how to select the right tool for your computer vision project. Your first choice will be a critical one: build vs. buy. You’ll also have at least six important data annotation tool features to consider: dataset management, annotation methods, data quality control, workforce management, security, and integrated labeling capabilities.