Fruit Inspector

build 0.10.0 · Windows x64 · Nim · ONNX Runtime (CPU / OpenVINO / CUDA via settings.json) · line station

Fruit Inspector is conveyor inspection: the fruit moves on the belt; quality is graded only in the control zone, not anywhere in the frame.

EdgeInfer: https://github.com/olesha-ai/edgeinfer-eval

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Pictures

Test bench: Intel i5-11400, 32 GB RAM, no discrete GPU, USB camera 1920×1080.
ORT CPU EP: infer loop on the order of ~100–130 fps (see speed.jpg).
Camera preview ~30 fps — camera limit, not infer.

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Idea

On the line you do not grade an apple “wherever it happens to be in the frame”. You grade it in the control zone — a fixed spot on the belt.

The image uses a 3×3 grid. The centre cell (C) is the control zone:

• detect, track, and stable IDs on the line (up to two apples for now);
• focus — the apple closest to cell C; after it leaves C — sticky focus on the next ID; a new detection does not steal focus;
• Cat (LightGBM) — only while the focus apple’s centre is inside C;
• pipeline: YOLOX (640) → ROI refine (256) → 44 features (fruit_features) → Cat → GOOD / BAD on screen.

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Station behaviour

• no full-frame chase along the belt — we work cell C and an ID queue;
• while the apple is moving toward C — detect and track only, Cat is not run;
• in C — feature collection and verdict;
• left C — sticky on the next ID, repeat.

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trig

On the desk, Cat starts as soon as the apple enters C. On a live belt that is not the final story: the fruit can still move, roll, bounce — the frame is already in the zone, but the object is not ready to grade. On the factory floor the moment is different: stop, lying still, ready to capture.

trig is groundwork for that. Not aim-by-coordinates, but a station signal: object ready — grade it. The inspector sends the verdict over the network (GOOD / BAD and ID); trig shows the result and will become a line command over time: stop the section → signal the model → get the grade. The archive already includes trig as a LAN client; the full chain “ready → command → Cat” is the next step.

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Stack

• Nim, ImGui (GLFW)
• ONNX Runtime — provider in settings.json: cpu, openvino, cuda
• YOLOX-nano + LightGBM (Cat ONNX)
• fruit_features.dll + features.json next to Cat
• apple crop pack: model/apple/det.onnx, cat.onnx, features.json

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What’s in the zip

• Fruit_Inspector.exe
• fruit_features.dll
• lib/cpu/, lib/openvino/ (and lib/cuda/ if included)
• model/apple/
• settings.json
• trig.exe — LAN client (see trig)
• licenses/

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settings.json

The file sits next to the exe. Values in the archive may be from my desk — use your own.

Engine: ort_provider, ort_intra_threads, ort_inter_threads, openvino_* (when using OpenVINO).

Models and camera: crop_pack, model, cat_model, camera_index, conf, iou, infer_main_tensor_size, infer_refine_tensor_size.

Output and logs: ip_addr, ip_port, memory_diag_enabled.

Edit the file by hand or use Settings → Apply.

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Run

1. Unpack locally.
2. Keep lib/, model/, fruit_features.dll, and settings.json next to Fruit_Inspector.exe.
3. Set camera_index and ort_provider.
4. Run from cmd if you want [infer] fps lines on stdout.
5. START / STOP in the UI; Esc asks before quit.

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License

Testing, teaching, research, feedback — yes.
Commercial production under this license — no.
Full text: LICENSE.md, licenses/Fruit_Inspector_LICENSE.md.
Third-party components: licenses/THIRD_PARTY.md. Do not remove the licenses/ folder from the zip.

Commercial contact: olesha-ai.

Intel, OpenVINO, Microsoft, Megvii, and other names are trademarks of their respective owners.

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olesha-ai