The problem Fleet Fox solves

• A working infrastructure wherein, when a vehicle passes through a checkpoint and the solution identifies as many features as it can regarding the situation of the vehicle like
  o Vehicle entry/exit
  o Vehicle details

• Cost effective solution that can be implemented easily within Indian markets/or similar regions.

• DarkNet Deep Learning model was used to in detection of number plate of vehicle.

• Can have a track of all vehicles details inside a particular premises such as

  o AssetID - Unique identifier of the asset
  o AssetName - Name of the asset (Make|SerialNumber)
  o DriverName/OperatorName - Name of the asset operator
  o EntryType - Type of entry/exit
  o GateNumber - Number of the gate through which the vehicle entered/exit
  o EventTime - Event date and time at which the vehicle entered/exit
  o LatLon - Latitude or longitude of the asset
  o Location - Location of the asset

• The solution can alert the management of illegal vehicle inside the premises.

Challenges we ran into

• Training Deep learning model was time consuming,
• We ran into issues related to REST API .
• We were new to Django Rest Framework and it took us quite some time to learn it.
• CORS error in API fetching using axios.
• Creating Api Logics was tough.
• Routing in React JS using React routers 6 .