WasteWatch

Task distribution and planning out our goals to accomplish for the weekend.

Introduction

Along with Samar Qureshi, Joe Dai and Lee Zhang, WasteWatch was created as a solution to the amount of medical supply waste that hospitals face. WasteWatch offers a comprehensive IoT approach, capturing data on discarded items across multiple trash receptacles, and determines the specific disposals at each garbage station. This data is then centralized and displayed for interpretation at a larger scale.

Project Details

Each trash can is fitted with a node that houses a Raspberry Pi Model B, which is equipped with a camera, an MPU 6050 accelerometer using the I2C protocol, and LED indicators. The system is activated when the trash bin's lid opens beyond a 50-degree angle, triggering the LEDs and initiating a video stream to our core processor - the NVIDIA Jetson Orin Nano. An integrated low-pass filter in the MCU minimizes accelerometer noise from nearby vibrations, ensuring accuracy by eliminating false positives. The video stream is transformed into image frames and then converted to binary for batch data transmission via SocketIO. On the receiving end, these frames are stored in-memory using BytesIO and subsequently fed into our inference model, which was crafted and honed using the TensorFlow/Keras API and a proprietary dataset. The resultant classifications are then stored in the CockroachDB, a serverless database, and simultaneously relayed to the user interface.

To learn more

Check out the repo or the Devpost below.