We aim to provide the higher authorities responsible for sanitation and cleaning of large areas or intuitions a method for evaluation the work of their subordinates without manually inspecting the areas which might be kilometers apart.

• Aerial Images taken using a drone will be inferred via a customyolov5 model which was trained on images of trash
• If the number of detections are more than a set threshold an alert is sent to the authorities in the form of an Incident in ServiceNow
• The inferred images are also stored in Amazon S3 with a reference of them in the database for reference
• The images corresponding to a given flight can be viewed via our Web Client

• The Drone Command app can do the following tasks
  1. Add new missions :- It uses GoogleMaps API to facilitate the user to add new missions for the drone to perform and also stores them in MongoDB
  2. Command the drone :- The app uses WebSocket API to command the ground station to perform a flight based on the mission chosen by the user
  3. Live Tracking :- The user can track the drone on a map as it completes it's missions to track it's progress live using the same Web Socket connection.
• Tech Stack :- Flutter,Websockets API,REST API
• Repository :- https://github.com/srikharshashi/drone-control

• DroneKit is a python API for communicating with the ground station and UAV's which wraps over the ArduPilot API for flight control leveraging the MAVLINK protocol
• The Flight Controller we used (PixHawk) can communicate with the Onboard Computer as well as the Companion Computer on the drone.
• The Onboard Computer enables the drone to click pictures programmatically when a way point is hit
• Camera used: GoPro Hero 9 Black
• The Ground station connects to the main server with a WebSocket connection
  1. Its listens for LAUNCH command
  2. It broadcasts it's location and it's update it's arming status to the status
• Once a mission is completed it creates a flight in the server for the given mission id
• The flight_id returned is then used for future purposes in Trash Detection Client
• Simulator used : Ardupilot Simulator (for local development)
• Tech Stack :- Ardupilot SITL,Dronekit SDK,Websockets using asyncio
• Repository :- https://github.com/srikharshashi/dronekit_websockets

• Trash Detection Client is a GPU enabled client for detecting trash from the images obtained by the drone
• The script which is run on a folder of images accepts a flight id and performs inference on those images and updates the flight details in the database based on number of inference
• The custom yolov5 trained model has an accuracy for 56%
• It also uploads these images to Amazon S3 and preserves a reference URL to them
• And finally after inference is done it creates an incident in Service Now

• Tech Stack :- Yolov5,REST API
• Repository :- https://github.com/srikharshashi/projectschool
• Data Set Used :- https://universe.roboflow.com/alexandros-petkos/marinelitter-4k-test

• The backend is an expressjs server with a MongoDB isntance hosted on Atlas .
• The hosting used is Amazon EC2 Free Tier paired with Amazon S3 as an iage storage service
• It acts a CRUD server as well as a WebSocket Server
• Resources
  1. Missions
  2. Flights
  3. Images
• It's responsible for the fetching the above resources ,location tracking as well as drone command
• Repository : https://github.com/srikharshashi/Mission-Store

• A react application is used to view the infered images stored in the DB
• All the images corresponding to a given flight id can be fetched and viewed on the webapp
• Repository :- https://github.com/Prudhvi472/Swach-Campus-Ngit
• Author :- Prudhvi Reddy