Project Info

Droneventory

💡

Inspiration

Having worked in the restaurant industry as a bartender, I noticed firsthand how challenging inventory management can be. Restaurants experience high inventory turnover because food and drinks spoil under varying conditions, such as fluctuating demand and seasonal changes. This makes inventory prediction difficult, and manual counting is both time-consuming and often inaccurate. These challenges inspired me to create Droneventory, a solution to automate and streamline the inventory process, saving time and reducing errors. 🚀

What it does

Droneventory uses a Crazyflie drone equipped with object recognition to automate inventory counts. By scanning shelves and recognizing objects, it provides real-time updates, significantly reducing inventory errors and saving labor costs. 🛠️

How we built it

At the hackathon, I assembled the drone using a Crazyflie board integrated with an accelerometer, a pressure sensor, an ESP32 module, and a GAP8 microcontroller with 8 cores for running lightweight machine learning models efficiently. I connected it to Amazon Rekognition, creating an API gateway via AWS S3 to recognize objects through the camera integrated with the ESP32 over Wi-Fi. 🧩

Challenges we ran into

The biggest challenge was that the drone’s bootloader was outdated and didn’t support over-the-air flashing with the Crazyradio antenna. I needed a JTAG cable to flash my code, but none were available at the event. Unfortunately, my Amazon order for the cable is set to arrive after the hackathon. 🎉

Accomplishments we're proud of

I'm proud to have made my first contribution to the open-source Crazyflie drone project, which is used by labs and research institutions worldwide. While my code hasn’t been accepted yet, submitting a pull request to such a significant project was a great milestone for me. 📚

What we learned

I learned a lot about the SPI communication protocol and how microcontrollers like the ESP32 and GAP8 work. I also gained experience in reading open-source projects with large codebases, identifying and modifying the right files for my contribution. This project deepened my understanding of how to interface with sensors and control hardware for drone navigation. 🔮

What's next

Next steps include finding a co-founder, refining the minimum viable product, and testing the system with local restaurants. Once proven, I plan to seek clients and scale the solution further.

Analysis

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Metric

No commits on this project resolved to a GitHub account.

Technology

Found in codeClaimed only

CIn code
PythonIn code
TensorFlowIn code

3 of 3 appear in the indexed code.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

547 KB

Source files

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

Feature verification

1 Verified2 Code-supported3 Claimed only2 Blocked

AI-deck GAP8 examples repository with documentationVerified
AI-deck examples repository with starting guides documentation
Claimed on readmehigh confidence
- README.md:1 — README confirms this is the stock Bitcraze AI-deck GAP8 examples repository pointing to external documentation
- docs/index.md — docs directory contains the generic guides referenced by the README
ESP32 camera streaming over Wi-FiCode-supported
Camera integrated with the ESP32 over Wi-Fi
Claimed on Devpostlow confidence
- examples/other/wifi-img-streamer/wifi-img-streamer.c — Stock Bitcraze example streaming camera images over Wi-Fi from the AI-deck (GAP8+ESP32); present in this repo but generic, unmodified, and not wired to any inventory/Rekognition pipeline
- examples/other/wifi-img-streamer/opencv-viewer.py — Companion Python viewer receiving the streamed images over a socket, not an HTTP upload to AWS
GAP8 8-core microcontroller running lightweight ML modelsCode-supported
A GAP8 microcontroller with 8 cores for running lightweight machine learning models efficiently
Claimed on Devpostlow confidence
- examples/ai/classification/classification.c — Stock GAP8 TFLite image classification example present in the repo, demonstrating the general capability but not customized for inventory/food object recognition
- examples/ai/classification/model/classification_tflite_files.zip — Pretrained model artifact bundled with the stock example, unrelated to any specific inventory training data
Amazon Rekognition integration via AWS S3 API gateway for object recognitionClaimed only
Connected it to Amazon Rekognition, creating an API gateway via AWS S3 to recognize objects through the camera integrated with the ESP32 over Wi-Fi
Claimed on Devposthigh confidence
Crazyflie drone with object recognition for automated inventory countingClaimed only
Droneventory uses a Crazyflie drone equipped with object recognition to automate inventory counts by scanning shelves
Claimed on Devposthigh confidence
Real-time inventory updates from shelf scanningClaimed only
Provides real-time updates, significantly reducing inventory errors and saving labor costs
Claimed on Devposthigh confidence
Accelerometer and pressure sensor integration on the drone buildBlocked
Assembled the drone using a Crazyflie board integrated with an accelerometer, a pressure sensor, an ESP32 module, and a GAP8 microcontroller
Claimed on Devpostlow confidence
Open-source pull request contribution to the Crazyflie projectBlocked
Submitted a pull request to the open-source Crazyflie drone project
Claimed on Devpostlow confidence

An AI agent derived these features from the project’s Devpost page and readme, then searched the code for each one. Verified features are backed by cited code; claimed-only features had no supporting code, which is not by itself proof a feature is missing.