Project Objective
Large parking lots (say airports, universities, etc.) are studied to understand average parking availability with respect to time of the day and month. These reports are then used for future planning/expansion. Traditionally done using personnel on the ground, drones are now being used to do it within a fraction of the cost & time. Drone images captured are then analysed by human annotators which is an expensive process. Nanonets provides solutions to automate drone image analysis, again at a fraction of the cost & time.
Before
Traditionally, carrying out a parking study requires
several people walking around the parking lot and
manually counting the vehicles, multiples times in a
day. For instance, a parking lot management company
hired 6 personnel on ground to survey a parking lot
consisting of 3,000 parking spaces. Data was collected
on an hourly basis, 12 hours a day, for a period of 1
week. The data analysis and report generation
typically took another week to complete. The data
collection is prone to human error, partly due to the
tedious and mundane nature of the task.
With the increase in availability of cheap camera
mounted drones, it is not surprising that the use of
drones for parking lot studies is on the rise.
After
By comparison, the same 3,000 spaces parking lot can
be covered by a 15 minutes drone flight. The drone
flies along a pre-planned route over the parking lot
collecting overhead images at regular intervals. These
images are then stitched together to form a huge
orthomosaic image. Nanonets web API can then be used
to get the count of cars in the orthomosaic image
created each hour.
Each orthomosaic image is processed within seconds and
the results are sent back to the server machine over
the web. 1 human annotator then verifies the results
of the API output and collates the results in his
report.
Building a Nanonets Model
Building a custom Nanonets model for car counting took 3
days to build with training data of a 1,000 images. The
easy-to-use APIs ensured that the integration and testing
of Nanonet's custom built models was completed within a
week.
This way, the use of drone and Nanonets APIs helped reduce
the need for manual labour on ground and achieve a faster
& more accurate analysis.
Humans involved for a parking lot study
Without Nanonets
8 persons
After integrating NanoNets API
2 persons
Cost of a typical parking lot study
Without NanoNets
$$$
After integrating NanoNets API
$