San Jose, Oct 5 2017 – FlytBase Inc. a startup building developer platforms for connected
intelligent drones, has announced the release of its AI Platform for Drones. FlytBase has
built the world’s first IoT platform for commercial drones, the “Internet of Drones” (IoD)
platform. Continuing on its mission to bring intelligence and connectivity to commercial
drones, FlytBase is now extending its cloud and edge compute platforms to incorporate AI
and machine learning.
As drones continue to find use in wide range of commercial applications, the focus is
shifting towards higher levels of automation and tighter integration of drones with
business processes, for significantly improved efficiencies. Recent advances in AI have
enabled computers to makes sense of the visual data around them, almost reaching
human level performance in some cases. Some of the tasks enabled by these algorithms,
- Object detection – identify and locate objects of interest in an image
- Object counting – identify and count objects of interest in an image
- Image segmentation – classify pixels in an image into multiple finite segments to
- Change detection – detect changes between two temporally spaced images
- Image classification – classify an image into one of the known categories of images
The technological potential of drones is being further enhanced by combining autonomous
drone tech with AI. Computer vision systems, mounted on drones, enable them to gather
rich visual data either in the form of photos or videos. Processing this data using AI unfolds
unique perspectives and information, which otherwise would be either impossible or very
expensive to derive using traditional techniques involving human effort.
With the vision to leverage AI for drone applications, FlytBase platform is being further
extended to incorporate AI capabilities to process aerial image data.
FlytBase AI platform is based in the cloud, wherein the entire workflow of preparing
datasets, training models and deploying trained-models for inferencing has been
automated. This enables quicker turn around time and faster iterations when a use case is
being worked upon. Being in the cloud also helps in scaling the system up at runtime when
demand (either for training, or for real-time inferencing) increases.
Examples of use cases that can be automated with FlytBase AI platform, are:
a. Object counting – e.g. counting the number of Arabian Oryx from an orthomap
image. These are an endangered specie and keeping a tab on their count goes
towards their conservation.
b. Object detection – e.g. locating cracks and rust areas from an image of industrial
c. Change detection – e.g. detecting changes between two photos of a parking lot
taking from almost the same vantage point at different times.
To harness FlytBase AI platform capabilities, customers bring in their use-case to FlytBase,
along with sufficient training images dataset.
The customer-provided data is carefully cropped, labeled and packaged for training
purposes, and added to an Image Dataset Library.
The FlytBase AI model-training workflow consists of:
- Model Library: Hosts object detection models to choose from during training
- Pre-trained weights library: Hosts weights from previously trained models to borrow
- Image dataset library: Hosts packaged datasets provided by customers. The raw
data is pre-processed for image augmentation and labeling before putting into this
Via the above workflow, user can select various pieces of the training pipeline and initiate
training on one of our GPU enabled cloud compute nodes. This results in a trained model
ready for inferencing.
Once our model is trained, it is deployed on the platform for direct use by our users. Users
can do live inferencing either via our web console, or by using REST API’s exposed by the
platform. REST APIs have the added advantage of integrating this platform with customer’s
system for further automation.
FlytBase AI platform is designed to support multi tenancy, which enables utilisation usage
of resources, and hence cost savings for our customers.
Deep Learning Algorithm
At the heart of the image-processing pipeline are state-of-the-art CNN models employing
recent advancements in computer-vision and deep-learning.
Over the last few years, several object detection models have been published, which have
significantly improved upon the previous generation, in terms of accuracy and speed of
inferencing. Notable are, SSD, DetectNet, Fast R-CNN, Faster R-CNN, Yolo and Yolo V2.
Similarly, for image classification, ResNet50, VGG16/19 and Inception models are some of
the most preferred models. Some models have better accuracy, while others might be faster at inferencing than others. Selection of a model takes into account these criteria, tailored to customer’s use case.
The pipeline allows several model implementations (same model with different
hyperparameters, or different models altogether) to be trained on the same dataset,
simultaneously, so that the best can be chosen. Since different model implementations
might need datasets to be arranged in different formats (e.g. from PASCAL VOC to
TFRecord format), we have built adapters to transform the data on the fly to suit the model.
We have used transfer learning to tune the off-the-shelf pre-trained models for getting
higher accuracy for detecting our object(s) of interest. This involves removing layers of the
off-the-shelf pre-trained models to keep the correct level of representation from previous
dataset, before training them on new dataset.
The FlytBase AI platform is agnostic to the particular framework in which the models are
implemented (Tensorflow, Caffe, Theano etc. ), by virtue of an abstraction layer. This allows the platform to assimilate the best implementation of cutting edge models coming out of research labs, with ease.
Challenges and Solutions
Using high resolution aerial images to train computer vision models poses unique
a. Lack of sufficient training data: There are plenty of open training datasets out there,
but almost all of them have images taken from human eye level. What makes aerial
images unique is their top-down view of the objects. Moreover, for custom object
detection, customers don’t often have enough images to train the model on,
wherein we have to make do with limited set of images.
b. Very high resolution of the images: Computer vision models can process images of
limited resolution at a time. For high resolution images, we need to crop the images
into sizable chunks and run inference on them one at a time. This can lead to double
counting or misses.
c. Shallow features of objects: When looked down from the top, objects can have very
generic shapes which a) can be hard to detect and b) can appear to be similar to
FlytBase AI platform uses various approaches to address these challenges, including data
augmentation, cropping with different offsets for hi-res images, and training models on
similar looking objects for better differentiation. Improving algorithms to address these
challenges is a continuous process, further enriching the platform.
A Case Study: Arabian Oryx Detection and Counting
The Arabian oryx or white oryx is a medium-sized antelope native to desert and steppe
areas of the Arabian Peninsula. It was extinct in the wild by the early 1970s, but was saved
in zoos and private preserves, and was reintroduced into the wild starting in 1980.
Keeping an up to date record of their population, with geotags, is an essential part of their
conservation. The practice has been to count them manually by looking at a high resolution
ortho image of the sanctuary. This is error prone and time consuming. Moreover, our
customer had a backlog of such images taken over time, where the counting had to be
done on all of them.
At FlytBase, we developed an image processing pipeline to automate the task of detecting
oryxes in an ingested image and count them.
We started with 400 images which constituted an orthoimage, and extracted 1000×600
sized images, which had either oryx in them, or oryx like objects. These were then labeled
and packaged into training and testing datasets in the Pascal VOC format.
A Faster R-CNN based object detection pipeline was set up in the cloud using the tensorflow object detection library. In the pipeline, the images were augmented by horizontally flipping and random resizing. Once everything was in place, the model was trained for 10k iterations.
The model so prepared could scan a 1000×600 sized image for Oryx. But it had to run on a
high resolution orthoimage (29200×24160 pixels). To meet that requirement, the
orthoimage was split into sections sized 1000×600. Detection was executed on them,
individually, and the results were stitched back. To avoid double counting or misses, the
split-detect-stitch procedure was repeated with different offsets for the splits, and the
median count of all the runs was obtained.
The results from this model were quite accurate and impressive. The FlytBase AI platform is able to process aerial image-data, gathered over several months, in order of minutes. This was the world’s first application of machine learning on drone image-data for Oryx
detection in the desert.
The Road Ahead
There is a vast potential to be unlocked for our customers, from the images they collect via
drones. With its scalable architecture, automated pipeline, and with our vast experience in
dealing with drones, their data and automation, FlytBase AI platform will result in
significant improvement in efficiencies for our customers.
FlytBase AI platform is optimised for interpretation of drone data, and it seamlessly
integrates with the rest of FlytBase platform to offer connectivity with your business
applications. If you are looking to leverage machine-learning technology for automation of
your drone data-processing, please reach out to our experts at firstname.lastname@example.org or
signup at flytbase.com/ai