Drones generate vast amounts of data, which is usually in the form of images or video streams. Identification of objects of interest, counting them, or detecting change over time, are some of the tasks that are monotonous and labor intensive. FlytBase AI platform offers a complete solution to automate such tasks. It has been designed and optimised specifically for drone applications.

1. AI Powered Drones
Enable object detection, object counting, change detection
and much more on drones

2. Drone data and AI
Drones generate vast amounts of data, which is usually in the
form of images or video streams. Identification of objects of
interest, counting them, or detecting change over time, are
some of the tasks that are monotonous and labor intensive.
FlytBase AI platform offers a complete solution to automate
such tasks. It has been designed and optimised specifically for
drone applications.
The cloud-based training system leverages the scalability of the
cloud to accelerate the training of models, to suit various
customer requirements. Based on the use-case, the trained
model can be deployed in the cloud (for post-processing of data)
or on the edge (for real-time analysis).

3. 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.
Object Detection
Identify and locate objects of
interest in an image.
Change Detection
Detect changes between two
temporally spaced images.
Object Counting
Identify and count objects of
interest in an image.
Image Classification
Classify an image into one of
the known categories of
images.
Image Segmentation
Classify pixels in an image into
multiple finite segments to
simplify representation.
Drones, AI and its applications

4. Business Use Cases
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 turnaround 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.
Cattle or Animal Counting
Counting the number of Animals
from an orthomap image. These can
be endangered species and keeping a
tab on their count goes towards their
conservation.
Construction and
Infrastructure
Locating cracks and rust areas
from an image of industrial
structures.
Parking lot and Traffic
Management
Detecting changes between two
photos of a parking lot taking from
almost the same vantage point at
different times.

5. To harness FlytBase AI platform capabilities, customers bring in their use-case to FlytBase, along with sufficient training images
dataset.
Harness FlytBase AI platform

6. FlytBase AI training workflow
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 representation
● Image dataset library: Hosts packaged datasets provided by
customers. The raw data is pre-processed for image augmentation
and labeling before putting into this library

7. 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.
Inferencing via a dashboard or APIs

8. 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 prefered 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.
Fig. Several models can be trained simultaneously, and the
best chosen

9. The pipeline allows several model implementations (same model with different hyper-parameters, 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.
State-of-the-art neural network

10. 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.
High resolution of the
images
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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 other objects.
Shallow features of
objects
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Challenges and Solutions
Lack of sufficient
training data
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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.
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.

11. Get in Touch
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
letstalk@flytbase.com
OR
Visit flytbase.com/ai