gracefullight/pipevision-yolo26m-seg

PipeVision YOLO26m-seg — Pipeline Defect Segmentation (ONNX FP16)

Model: yolo26m-seg-pipevision-fp16 Format: ONNX FP16 (opset 17) Input: images [1, 3, 640, 640] — NCHW, float16, RGB normalized [0, 1], letterboxed (pad 114) Outputs:

• output0 [1, 100, 38] float32 — end-to-end (NMS-included): x1, y1, x2, y2, conf, classId + 32 mask coefficients per detection
• output1 [1, 32, 160, 160] float16 — mask prototypes

Team: Plumbers of UTS (Bo Zhao, Jadyn Braganza, Eunkwang Shin)

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Model Description

This model performs instance segmentation of structural defects in pipeline CCTV inspection images. It is exported from a YOLO26m-seg PyTorch checkpoint (best.pt) trained on a 6-class pipeline-defect segmentation dataset.

The model is exported end-to-end with NMS included in the ONNX graph (output0 already carries post-NMS detections capped at 100). Client code decodes the fixed [1, 100, 38] tensor directly — no separate NMS pass is required, though PipeVision still runs a light client-side IoU dedup as a guard.

Each detection's instance mask is reconstructed from the 32 mask coefficients and the shared output1 prototypes: sigmoid(coeffs · prototypes), thresholded at 0.5, cropped to the box and resized to image space.

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Intended Use

• Browser-based pipeline defect visualization (client-side ONNX Runtime Web)
• Academic research and portfolio demonstration
• Offline inspection tool prototype

Not suitable for: Safety-critical production deployment, real-time video streams, mobile-only inference (model is ~45 MB).

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Training Data

Dataset: Pipeline-defect instance-segmentation set (custom, YOLO polygon format)

Split	Images	Notes
Train	15,484	70%
Validation	3,319	15%
Test	3,319	15%
Total	22,122	—

Classes (6 total):

ID	Class Name	Severity
0	Deformation	high
1	Obstacle	high
2	Rupture	critical
3	Disconnect	critical
4	Misalignment	medium
5	Deposition	low

The class set follows a long-tail distribution — Deposition and Disconnect are the rarest categories in the test split.

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Training Configuration

Parameter	Value
Architecture	YOLO26m-seg
Parameters	21.8M
Input resolution	640 × 640
Epochs	150
Task	Instance segmentation
Export	ONNX FP16, opset 17, nms=True
Framework	Ultralytics (PyTorch)

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Metrics

Validation-set metrics at the final training epoch (results.csv, epoch 150):

Metric	Box	Mask
Precision	0.923	0.827
Recall	0.891	0.793
mAP@0.5	0.931	0.788
mAP@0.5:0.95	0.682	0.480

Per-class (validation, per_class_metrics.csv)

Class	AP50 (box)	AP (box)	AP50 (mask)	AP (mask)
Deformation	0.904	0.561	0.723	0.349
Obstacle	0.956	0.713	0.920	0.637
Rupture	0.910	0.628	0.849	0.501
Disconnect	0.969	0.824	0.753	0.521
Misalignment	0.986	0.757	0.769	0.455
Deposition	0.954	0.648	0.778	0.448

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Inference Defaults

conf_threshold: 0.25
iou_threshold: 0.45
max_detections: 100

ONNX Runtime Web Example (TypeScript)

import * as ort from 'onnxruntime-web';

const session = await ort.InferenceSession.create(MODEL_URL, {
  executionProviders: ['webgpu', 'wasm'],
});

// Input: preprocessed Float16Array [1, 3, 640, 640] (letterboxed, RGB/255)
const feeds = { images: new ort.Tensor('float16', data, [1, 3, 640, 640]) };
const results = await session.run(feeds);

// output0 [1, 100, 38]: rows of [x1,y1,x2,y2,conf,classId, ...32 mask coeffs]
//   — already NMS'd; drop rows with conf < threshold.
// output1 [1, 32, 160, 160]: mask prototypes for mask reconstruction.
const detections = results['output0'];
const prototypes = results['output1'];

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Limitations

1. Long-tail dataset — rare classes (Deposition, Disconnect) have fewer training instances; mask AP@0.5:0.95 trails the box metric on these.
2. Single domain — trained on one dataset source; performance on other pipe inspection cameras may differ.
3. FP16 precision — minor numerical differences vs the FP32 baseline.
4. No temporal context — designed for single-image inference, not video.
5. Validation metrics — numbers above are validation-set (no held-out test split was evaluated separately).

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License

MIT — Free to use for research and non-commercial projects.