Edge Waste Sorting: Material Classes, the Chinese Four-Way Category and MQTT

Usage notice

The Chinese four-way mapping is a table maintained by this project, not a ruling by any authority, and city rules differ; the output must not be the sole basis for fees, penalties or compliance decisions.

What this solution does

One trigger (a button, an HTTP call, or motion in the frame) makes the device take one image, classify the item into one of eight material classes, look up the Chinese four-way household-waste category from that class, and publish one MQTT message. An asynchronous callback receives the four-way result at the same time, so a flap, relay or lane indicator can act on it.

• One head, two levels of answer. The model predicts eight classes: paper, cardboard, glass, metal, plastic, textile, organic, residual. The four-way category (recyclable / kitchen waste / hazardous / other) is a table lookup on the eight-class argmax, so adapting to a local rule means editing the table, not retraining.
• One trigger, one capture. Button, HTTP or motion detection with an 800 ms debounce; a trigger that arrives while the previous one is still running is merged, not queued. A continuous mode also exists; it is rate-limited and publishes only after three consecutive frames agree on top-1.
• Payloads are validated before publishing. Every payload passes the event schema, plus two rules JSON Schema cannot express: category must equal top3[0], and confidence must equal top3[0].confidence. Failures are counted and dropped.
• An optional open-vocabulary track. A SigLIP 2 vision tower scored against constant text prototypes, selected at deploy time with model.track: open_vocab. It adds classes without retraining, answers in Chinese or English from the same image embedding, and yields a "not in my vocabulary" score.
• Actuator interface with no pin binding. The runtime calls back with a category; which pin it drives is decided by integration code, so one build runs on boards with different headers.

Image bytes never leave the device; the payload carries only a path or an object-store URI.

• Packaging source and deployment guide: sensecraft-solutions / solutions/edge_waste_sorting
• Picking a configuration and deploying: reference design page

• Ready to run on the device

  Install the application package and it runs; a first deploy takes about 35 minutes. Eight-class top-1 0.8877, four-way top-1 0.9500 (7417 validation images, CPU onnxruntime offline baseline, see performance and measured data).

• Open source

  Runtime code is Apache-2.0; the training data, TrashNet (MIT) and GC3 (CC BY 4.0), both permit redistribution.

• Your own camera, systems and actuators

  USB / RTSP camera or a file source; results go out over MQTT, POST /trigger can be called by a kiosk or PLC gateway, and a GPIO callback drives a flap or indicator.

• Runs entirely on site

  Capture, classification, lookup and the MQTT broker all run on the host; images stay on the device and the main chain needs no internet access.

What it looks like running on a device

Below is the drop-area framing seen by a reCamera PoE after the waste-sorting application package was installed. Nothing is placed in front of the camera; the image is a framing reference only.

Classification results go straight to MQTT. Below are consecutive events captured by subscribing to waste/recamera-cvi/results:

What hardware you need

Three things at the drop point: a trigger, a camera, a host.

① Trigger — a button wired to the host, POST /trigger on port 8080, or motion in the frame. All three share one 800 ms debounce, produce the same event, and are told apart by the trigger field in the payload.

② Camera — any USB, RTSP or file source looking down on the drop area; any model will do. A single item must take up a usable share of the frame, with only one item in view. Items that are too small reduce classification quality, and none of the numbers under performance and measured data come from such framing.

③ Sorting host — decides which model paths you can use.

Sorting host	Accelerator	Classifier as deployed	Open-vocabulary track	When to choose it
reComputer J3011 (Jetson Orin Nano 8GB)	Orin GPU	EfficientNet-Lite0 224², TensorRT FP16	Not offered	One drop point, baseline classifier only
reComputer J4012 (Jetson Orin NX 16GB)	Orin GPU	EfficientNet-Lite0 224², TensorRT FP16	Offered (the only device class that offers it)	You expect to add item classes later without retraining
reComputer R2000 series (Hailo-8)	Hailo-8	EfficientNet-Lite0 224², INT8 HEF	None	You have or want Hailo-8 hardware; the HEF is compiled off-device and downloaded at deploy time

Both reComputer models come in a fanless industrial enclosure (reComputer Industrial J3011 / J4012) with the same module and runtime, for a control cabinet or outdoor housing.

The open-vocabulary column comes from the resource notes in the device catalogue: the SigLIP 2 vision tower is a 371 MB ONNX at p50 66.93 ms per image as a CPU onnxruntime offline baseline and needs an accelerator.

④ Everything else — an MQTT consumer (the package runs a local broker on 1883); if the design has a flap or lane indicator, the driver hardware plus integration code for the GPIO callback, which ships bound to no pin. The main chain needs no internet access.

How to deploy on site

Settle the framing and trigger first, then install the software. Rated intermediate, about 35 minutes for a first deploy.

One: install the hardware, framing and trigger

One item per image, large enough

There is no detector in this chain. Two items in one frame produce one answer, and which item it describes is undefined; items that take up too small a share of the frame classify worse. Mount the camera looking down on the drop area so a single item fills a usable share of the frame, and decide before deployment whether the trigger is a button, HTTP or motion detection.

Two: install the software

Per-device steps are on the reference design page, where picking a configuration for your site also gives you the matching application package to download.

Pick a configuration and download 🖱️

Both presets follow the same steps:

1. Pick a preset and a host. Camera + reComputer J30 / J40 (Orin) or Camera + reComputer R2000 series (Hailo-8).
2. Deploy the waste-sorting runtime (required). On Orin the TensorRT engine is built on the device during deployment; the engine is tied to the GPU architecture and TensorRT version and cannot be precompiled for distribution. On Hailo the deployment steps check three Hailo ABI gates and then download the HEF.
3. Watch live classification (optional). The local panel on port 8080 has an MJPEG preview, health counters and the top-3 of recent results. Confirm the framing here before wiring anything.
4. Wire the trigger and confirm one classification (required). One button press, HTTP call or motion event should produce exactly one MQTT message on waste/<stream-id>/results, with category and confidence matching top3[0].
5. Switch to the open-vocabulary track (optional, Orin only). Set model.track: open_vocab at deploy time.

What interfaces are available

The sorting host exposes two interfaces and one in-process callback, all served by the host itself.

• Operations or record-keeping system — subscribe to waste/<stream-id>/results on port 1883. One JSON per classification.
• Kiosk UI, PLC gateway or test script — POST /trigger on port 8080 triggers one capture and classification; /events returns recent results with their top-3.
• Flap, relay or lane indicator — an asynchronous GPIO callback carries the four-way result. It is bound to no pin; the binding code is integration work.

Full interface table

Type	Where	Content	Notes
mqtt	1883, waste/<stream-id>/results	type, version, taxonomy_version, device, stream_id, frame_id, timestamp, trigger, inference_time_ms, pipeline_ms, category (class_id, class_name, china_category, china_category_zh), confidence, top3[], image_ref, model (name, backbone, input, onnx_sha256, accelerator)	One per classification. Validated against the event schema before publishing
http	8080, /trigger /preview.mjpg /healthz /events	POST /trigger fires one capture and classification (trigger recorded as http); live MJPEG; a health endpoint with inference time, trigger and debounce counts and MQTT counts; recent results with top-3	Local panel, no auth
GPIO callback	In process	Four-way result, asynchronously	actuator.enabled defaults to false. Bound to no pin

Two things to watch when reading fields:

• The image is never in the payload. image_ref.kind is none, local or object_store. Base64 image bytes in a payload violate the contract and are rejected before publishing.
• category always equals top3[0], and confidence always equals top3[0].confidence. Both are checked before publishing and failing payloads are counted and dropped, so consumers do not need to reconcile them.

The bundled MQTT broker allows anonymous connections for local commissioning; use a broker with credentials in a production deployment.

Performance and measured data

sensecraft-solutions / edge_waste_sorting

Measured on device: reComputer RK3588 series

Model / precision	Latency p50 / p95 (mean)	Agreement with CPU reference	Accuracy vs ground truth	Conditions
EfficientNet-Lite0 (m1c), fp16	7.906 ms / 8.129 ms (7.041 ms)	1.00	0.78	50 val images
EfficientNet-Lite0 (m1c), int8 calib64+normal	3.780 ms / 3.984 ms (3.807 ms)	0.90	0.72	63 calibration images, normal algorithm
EfficientNet-Lite0 (m1c), int8 calib64+mmse	3.785 ms / 3.981 ms (3.808 ms)	0.98	0.78	63 calibration images, mmse algorithm
EfficientNet-Lite0 (m1c), int8 calib256+normal	3.766 ms / 3.920 ms (3.500 ms)	0.90	0.72	252 calibration images, normal algorithm
EfficientNet-Lite0 (m1c), int8 calib256+mmse — recommended	3.803 ms / 4.003 ms (3.834 ms)	1.00	0.78	252 calibration images, mmse; agreement and accuracy match fp16, and it is 52% faster
SigLIP 2 vision tower, fp16	169.4 ms / 170.5 ms	Embedding cosine mean 0.999617, min 0.998841	—	191 MB .rknn

Conditions: converted with rknn-toolkit2 2.3.2, librknnrt 2.3.2, core_mask=AUTO, per-channel quantisation. mmse conversion takes 40–90× as long as normal (17.3 minutes vs 11.5 seconds at 256 calibration images), paid once at conversion and not at runtime.

Reproduce: solutions/edge_waste_sorting/evaluation/runs/2026-09-06-m1c-rk3588-radxa

On the reComputer RK3576 series only the SigLIP 2 vision tower and the superseded MobileNetV3-Small were run: SigLIP 2 vision tower fp16 p50 152.51 ms / p95 176.59 ms, embedding cosine mean 0.99965, min 0.99900.

Reproduce: evaluation/runs/2026-09-06-rk3576-cat

Baseline classifier accuracy: EfficientNet-Lite0 (m1c, shipped)

Metric	Value	Conditions
Material top-1 (8 classes)	0.8877	val, 7417 images; onnxruntime 1.25.1 CPU; ONNX e9f9e847…, 13,477,056 B
Material top-5	0.9833	Same
China four-way top-1	0.9500	Same; lookup on the eight-class argmax
macro-F1 (7 classes with samples)	0.8511	textile excluded — zero samples
Material top-1, held-out test	0.8802	test, 7290 images
Per-image inference latency, CPU	mean 16.796 ms / p50 14.724 ms / p95 28.718 ms	session.run only, CPU offline baseline, batch 1, not a device figure
Images with confidence below 0.5	318 (4.3%)	val
ORT PTQ INT8 vs fp32 agreement	0.965	200 val images, per-channel + MinMax, no collapse
Hailo DFC emulator INT8 vs CPU top-1 agreement	0.890	200 val images; accuracy vs ground truth 0.755 (CPU on the same images 0.795); from a build earlier than the shipped HEF, no board latency

Four-way top-1 (0.9500) is higher than material top-1 (0.8877) because confusion among glass, metal and plastic all lands in recyclables; four-way alone overstates how well the model tells materials apart.

Reproduce: evaluation/runs/2026-09-06-m1c-cpu, evaluation/runs/2026-09-06-m1c-hef

Open-vocabulary track: SigLIP 2 ViT-B/16

Metric	Value	Conditions
Material top-1 (8 classes)	0.8501	val, 7417 images; English prompt set waste8-en/v1, template t02, 16-shot α=0.8, temperature 0.0075
Material top-5	0.9987	Same
China four-way top-1	0.9393	Same; hierarchical path (eight classes, then mapping)
macro-F1 (7 classes)	0.7460	Same
ECE (15 bins)	0.0221	Same
Open-set AUROC	0.7538	Mean over the 7 classes with samples, leave-one-class-out, score = 1 - max softmax
Chinese–English agreement (same image)	material 0.8698 / four-way 0.9143	One visual embedding, three prototype banks
Material top-1, held-out test	0.8620	test, 7290 images
Per-image inference latency	p50 66.93 ms / p95 91.62 ms	CPU offline baseline, batch 1, vision tower only, not a device figure

Against the baseline (same split):

Metric	Baseline (MobileNetV3-Small)	Open vocabulary (SigLIP2-B/16)
Material top-1, val	0.8792	0.8501
Material top-1, test	0.8807	0.8620
China four-way top-1, val	0.9519	0.9393
macro-F1, val	0.8292	0.7460
ECE (15 bins), val	0.0308	0.0221
Open-set AUROC	None (a closed-set head cannot drop a class without retraining)	0.7538
Chinese–English agreement	No text side	0.8698 / 0.9143
Zero-shot new classes	Retraining required	Edit the prompt
CPU p50 latency	1.57 ms	66.93 ms

The baseline in this comparison is the superseded MobileNetV3-Small. The shipped EfficientNet-Lite0 reaches val top-1 0.8877 on this split at a CPU p50 of about 14.7 ms, which shrinks the latency ratio from 40× to 4–5×.

Reproduce: evaluation/runs/2026-09-05-w1-cpu

Deployment footprint

Item	Size
Baseline ONNX (efficientnet_lite0_waste8.onnx)	13,477,056 B
SigLIP 2 vision tower ONNX (siglip2_vision_224.onnx)	371,695,898 B
Prototype banks + calibration report	about 155 KB total

Runtimes and key parameters

Device	Model	Precision	Runtime	How the model reaches the device
reComputer J30 / J40 series	EfficientNet-Lite0 224²	FP16	TensorRT	Engine built on the device at deploy time, tied to GPU architecture and TensorRT version; cannot be precompiled for distribution
reComputer J40 series (optional open-vocabulary track)	SigLIP 2 ViT-B/16 vision tower + constant text prototypes	—	—	model.track: open_vocab
reComputer R2000 series + Hailo-8	EfficientNet-Lite0 224²	INT8 (uint8 HEF, optimization_level=2)	HailoRT 4.21.0	HEF compiled off-device with DFC 3.31.0, downloaded at deploy time
reComputer RK3588 series	EfficientNet-Lite0 224²	INT8 (calib256+mmse)	librknnrt 2.3.2	Converted off-device with rknn-toolkit2 2.3.2

• Trigger debounce: 800 ms; a trigger arriving while the previous one is still running is merged, not queued
• Continuous mode: publishes only when top-1 is the same for 3 frames in a row; trigger mode gives one answer per shot
• actuator.enabled: default false; the GPIO callback is not bound to any pin

Known degradation

• textile has zero training and evaluation samples: neither source dataset has a fabric class. The eighth logit is kept (ONNX output stays 1×8), but the model has never predicted it, and every table reports n/a for it.
• No material class maps to hazardous; it stays in the enum for schema stability and the current build never emits it.
• organic makes up 48.9% of train and 47.1% of val; its recall (0.9791) is far above the other classes (0.70–0.88), and the model pushes uncertain items toward it.
• residual has only 20 val samples; the open-vocabulary track reaches 0.2754 precision and leave-one-out AUROC 0.5795 on it, close to chance.
• On the open-vocabulary track, predicting the four-way class directly from Chinese prompts gives only 0.8478; the hierarchical path (eight classes, then mapping) gives 0.9393.
• The superseded MobileNetV3-Small collapses under INT8 on all three edge chains: Hailo DFC emulator agreement 0.115, reComputer RK3576 series 0.10, reComputer RK3588 series 0.22 (fp16 0.98–1.00); this is why the baseline moved to EfficientNet-Lite0. RK3588 INT8 results do not carry over to RK3576.
• The SigLIP 2 vision tower fails INT8 quantisation for Hailo-8 (hailo optimize errors at layer ne_activation_mul_and_add78), so there is no HEF; the open-vocabulary track does not run on the reComputer R2000 series.
• Both datasets are single-item photos (TrashNet on a white board, GC3 off-centre and often occluded), with no wet, crushed, stacked, backlit or partly bagged waste; accuracy at a live drop-off point will be lower than the figures in the tables.

Next steps

• Add the reComputer R2000 series (R2035-12, Hailo-8) material and four-way results on all 7417 validation images, and the trigger-to-answer latency and engine build time on reComputer J40 series (J4012), to "Performance and measured data".

Data and asset sources

• TrashNet — github.com/garythung/trashnet, MIT License, Copyright (c) 2017 Gary Thung. Based on two first-party sources: the repository's LICENSE file at commit 6fa2b87, and the license field of the official HuggingFace dataset card. MIT requires the copyright and licence notice to be kept and has no share-alike clause.
• Garbage Classification 3 — Material Identification (Roboflow Universe) — CC BY 4.0, stated verbatim in the README.dataset.txt shipped with the export. The project path on Roboflow Universe is material-identification/garbage-classification-3.
• SigLIP 2 — google/siglip2-base-patch16-224, revision 75de2d55…, Apache-2.0. Used by the optional open-vocabulary track only.
• MobileNetV3-Small ImageNet starting weights (torchvision) — BSD-3-Clause.
• Upstream runtime code — Apache-2.0.
• Architecture diagram — drawn from the package's device catalogue and output interfaces; contains no dataset images.

External material derived from these datasets must use the following attribution string verbatim:

TrashNet — Gary Thung and Mindy Yang, https://github.com/garythung/trashnet,
MIT License, Copyright (c) 2017 Gary Thung.
Garbage Classification 3 — Material Identification / Roboflow Universe,
https://universe.roboflow.com/material-identification/garbage-classification-3,
licensed CC BY 4.0.

No dataset-derived image is committed to this package. Both licences permit redistribution.