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Platform

BinEye.ai
Platform.

BinEye uses a Blues-connected camera module to capture scheduled images of bin contents and report the bin's location. AI-assisted material review and configurable contamination notifications are being introduced for upcoming deployments. A durable NFC identity supports tap-to-view access for each bin.

The Hardware

One module. Six jobs. Always on.

The BinEye Smart Module is a sealed, 7,000 mAh battery-powered edge compute unit factory-fitted to every Bin Eye Bin. It does six things simultaneously, without driver input, without wiring, and without a battery replacement programme.

Rechargeable Battery

7,000 mAh rechargeable lithium battery. Approximately 12 months under normal use. Rechargeable on the truck or at the dock — no site visit required for a charge.

GPS Tracking

GPS location (Full BINEYE Data tier). Reports when needed. Track history kept for approximately 12 months per charge.

AI Camera Vision

2 MP wide-angle camera. On-device inference using a model that being introduced for upcoming deployments, updated via OTA. Classifies fill level, material type, contamination, and overfill from inside the bin.

Fill Monitoring

Fill depth monitored continuously. Configurable alert threshold.

4G LTE Cellular

Global SIM. Works on local networks wherever the bin is deployed. Automatic carrier selection.

OTA Firmware

All firmware updates delivered over-the-air. No site visits. The model being introduced for upcoming deployments — every bin in the fleet gets smarter with each update.

The Data Pipeline

Edge to fleet dashboard in when processed.

Every telemetry event — fill reading, camera inference result, GPS fix, contamination flag — travels from the bin module through the cellular network to the BinEye cloud, is processed and classified, and surfaces in your fleet dashboard when processed.

The pipeline runs on AWS ap-southeast-2 (Sydney). Data never leaves Australian jurisdiction without explicit customer consent. Operators can also access all telemetry via the REST API — for direct integration with BinManager, Xero, dispatch systems, or custom fleet software.

01
Edge inference
Camera + ToF sensor → CNN model → event classification. All inference runs on-device before transmission. No raw images leave the bin by default.
02
Cellular uplink
Classified events + sensor readings + GPS position transmitted over 4G LTE. Encrypted in transit (TLS 1.3). Carrier-agnostic SIM auto-selects strongest available network.
03
Cloud ingestion
AWS ap-southeast-2 receives, validates, and time-stamps every event. De-duplication and gap-fill applied before storage. Full track history kept for approximately 12 months per charge.
04
Fleet API
REST API exposes fill level, GPS, material classification, contamination flags, and collection history. Webhooks available for real-time push to fleet dispatch and billing systems.
05
Dashboard + alerts
Web and mobile fleet dashboard shows live bin status, route optimisation suggestions, and configurable alert thresholds. SMS and push notifications on overfill or contamination events.
06
OTA update loop
Model improvements and firmware updates deployed from cloud → device OTA. The entire fleet updates automatically — no field service visits required.

The AI Model

A classifier that learns from every bin in the fleet.

The BinEye waste classification model is a convolutional neural network trained on annotated bin imagery from Australian commercial and residential sites. It runs on-device, updating automatically as the central model improves.

Architecture

Lightweight CNN — on-device

Quantised convolutional neural network optimised for edge inference on the module's embedded processor. Runs continuously without impacting GPS or telemetry.

On-device · No cloud round-trip per inference
Classification outputs

5 material classes + fill level

General waste, recycling, green/organic, construction & demolition, and hazardous/contaminated. Fill level output is a continuous depth reading fused with the camera estimate.

5 classes · continuous fill depth · confidence score per event
Training data

AU-specific annotated imagery

Trained on annotated imagery from Australian commercial and residential skip bin deployments. Dataset grows with each deployed bin — every classification event (with operator confirmation) feeds back into the training loop.

Fleet-scale training loop · improves with every bin
OTA updates

Fleet-wide model deployment

Model updates are tested, validated, and deployed OTA to the entire fleet simultaneously. No field service. No manual update. Every bin gets the latest classifier automatically — improving accuracy fleet-wide each release cycle.

OTA · zero downtime · rollback capable

Security & Compliance

Australian data. Australian jurisdiction.

Data residency

All telemetry, imagery, and account data stored in AWS ap-southeast-2 (Sydney). No data stored outside Australia without explicit consent.

Encryption

TLS 1.3 for all data in transit. AES-256 for data at rest. Cellular SIM communications encrypted end-to-end.

Privacy Act compliance

Operated in accordance with the Australian Privacy Act 1988 (Cth) and the thirteen Australian Privacy Principles (APPs).

Access controls

Role-based access controls. Operators see only their fleet. Least-privilege IAM on all cloud infrastructure. Audit logging on all administrative actions.

No raw image egress

Camera inference runs on-device. By default, no raw images leave the bin. Event-triggered image capture for contamination and illegal dumping evidence can be enabled per customer.

ChAFTA compliance

All hardware imported under the China–Australia Free Trade Agreement. 0% import duty. Full documentation available at order confirmation.

Build with Blues

// Pre-Orders Open — First Container Q4 2026

Every Bin Eye Bin ships
with this tech included.

Request a Quote → View Bin Catalogue

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