Deploying a crack-segmentation model at the camera is a systems project, not just a model export. Start by fixing the target device, camera, input shape, latency budget, power and thermal limits, and software stack. Then establish a reference result with the original model, convert it to a runtime the device supports, optimize one change at a time, and validate crack masks on representative images. Finally, benchmark the complete camera-to-result pipeline under sustained operating conditions.
There is no universally best edge board or runtime for an unspecified model. Operator support, tensor layouts, custom operations, accelerator compatibility and camera connectivity determine whether a deployment will work.
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What an edge deployment must include
An edge crack-inspection system normally contains six measured stages:
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- Preprocessing: resize, crop, color conversion, normalization and tensor-layout changes prepare the frame.
- Inference: the converted segmentation model runs on a CPU, GPU, NPU or an accelerator delegate.
- Postprocessing: logits become a binary or multiclass mask, often followed by thresholding, connected components, morphology or geometric measurements.
- Decision and storage: the application reports a defect, overlays the mask, stores evidence or triggers another machine.
- Operations: the device handles startup, camera failures, updates, thermal throttling, logging and rollback.
Model inference time alone cannot describe inspection performance. A fast model can still miss a line-rate requirement if capture, image transfer, preprocessing or postprocessing dominates the frame time.
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Define the target before choosing a runtime
Write a target specification before exporting anything. These values determine which conversion path and hardware delegate are realistic.
| Item | Record | Why it matters |
|---|---|---|
| Device | Board or embedded computer, operating system and processor generation | Determines available runtimes, drivers and acceleration backends |
| Accelerator | CPU, GPU, NPU or dedicated inference engine | Controls supported operators, precision and memory transfers |
| Model | Originating framework, input and output tensors, dynamic or fixed shapes, custom operators | Determines whether conversion can preserve behavior |
| Image contract | Sensor resolution, crop, color order, normalization and tensor layout | A mismatch can produce plausible-looking but incorrect masks |
| Performance | Maximum camera-to-result latency, required throughput and startup time | Sets the acceptance test rather than a headline FPS target |
| Physical limits | RAM, storage, power budget, enclosure, ambient temperature and cooling | Influences sustained speed and reliability |
| Connectivity | Camera interface, network access and whether operation must be offline | Determines the bill of materials and update method |
Also document the model’s preprocessing and postprocessing as executable specifications. Record whether channels are RGB or BGR, whether values are scaled to 0–1 or normalized by means and standard deviations, whether dimensions are height–width–channel or channel–height–width, and how the output tensor is converted into a crack mask.
Choose a runtime by compatibility, not by advertised FPS
Use the runtime whose converter and accelerator support match the model and device. A runtime is a candidate only after its operators, tensor types, delegates and camera integration have been checked against your exact artifact.
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| Path | Useful when | Important checks | Evidence limitation |
|---|---|---|---|
| LiteRT | You need an on-device framework with conversion, quantization and CPU, GPU or NPU execution. | Confirm that the originating framework, model operators, tensor shapes and target delegate are supported. Check the current CompiledModel and compatibility APIs before implementation. | General framework capability does not prove compatibility for an unspecified crack model and board. |
| ONNX Runtime on a Jetson-class device | Your model exports cleanly to ONNX and the platform can use its CUDA or other supported execution providers. | Verify provider registration, CUDA/cuDNN versions, unsupported operators, memory copies and camera capture integration. | The commonly cited Jetson example describes ONNX Runtime 1.4 and JetPack 4.4 from July 2020; treat it as an architecture example, not a current package recommendation. |
| TensorRT | You need an optimized inference engine on an NVIDIA platform and the model can be represented by supported layers or plugins. | Check ONNX parser support, dynamic-shape profiles, precision calibration, plugin requirements and engine portability across GPU generations. | Results from another segmentation architecture or device cannot predict crack-model performance. |
Keep a fallback CPU path during development. It is useful for checking numerical behavior when an accelerator delegate rejects an operator or silently partitions the graph.
Follow a reproducible deployment sequence
1. Freeze a baseline in the known-good environment
Run the original model where it is already trusted. Create a fixed validation set containing the intended surfaces, crack widths, image quality, lighting, camera distance and nuisance patterns such as joints, stains, texture and shadows. Store the source images, reference masks, preprocessing configuration, model checksum and software versions.
For each image, save the model output before thresholding as well as the final mask. This makes it possible to distinguish a numerical conversion difference from a thresholding or morphology difference.
Choose acceptance measures that reflect the inspection consequence. Common measures include intersection-over-union, precision, recall, missed-crack rate, false-positive area and the smallest crack width that must be detected. There is no universal acceptance threshold; the project owner must set one from the cost of missed defects and false alarms.
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2. Export and inspect the model graph
Use the official export path for the originating framework and the selected runtime. Before conversion, list every input and output tensor, shape, data type, quantization parameter, operator and custom layer. Identify control flow, dynamic dimensions and postprocessing that may not be included in the exported graph.
Do not assume that an export which completes successfully is semantically correct. A converter can replace, fuse or reject operations, and an application can still misread the output if channel order or output interpretation changes.
3. Convert with the least aggressive settings
Start with floating-point conversion when the target supports it. Run the converted artifact on the same validation images used for the baseline. Compare masks, probability maps and task metrics before enabling quantization, pruning or graph-specific optimizations.
Check these parity points explicitly:
- Input width, height, batch dimension and aspect-ratio policy.
- RGB/BGR order and alpha-channel handling.
- Scaling, mean subtraction and standard-deviation normalization.
- Tensor layout and contiguous-memory assumptions.
- Output tensor order, class meaning and threshold location.
- Resize interpolation, padding and crop coordinates.
- Handling of NaN, infinity, empty masks and out-of-range values.
4. Add the device accelerator
Run the same converted model through the intended CPU, GPU or NPU delegate. Capture which operators execute on the accelerator and which fall back to the CPU. A graph split across devices can be slower than a CPU-only graph because of synchronization and memory transfers.
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5. Optimize one variable at a time
After floating-point parity is understood, test reduced precision or quantization. Keep the baseline artifact and validation report so every change has a known comparison point. If a change improves speed but alters thin-crack recall or creates broad false-positive regions, it is not automatically an improvement.
6. Integrate capture and postprocessing
Connect the real camera and implement the exact production preprocessing path. Measure the mask after the same thresholding, morphology and geometric filtering that the inspection decision will use. A desktop test that reads already-resized images bypasses the most failure-prone parts of an edge system.
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7. Package a known artifact
Bundle the model, runtime libraries, preprocessing and postprocessing code, configuration, calibration data, camera settings and version manifest. Pin mutually compatible operating-system, driver and runtime versions. Keep a previous known-good package and a tested rollback procedure.
Quantization and other optimization choices
Quantization reduces parameter precision and can reduce model size and latency, but it can also change segmentation quality. Evaluate the actual crack masks after every optimization.
| Technique | What it changes | Toolkit estimate | Deployment caution |
|---|---|---|---|
| Post-training float16 | Stores weights at reduced floating-point precision | Up to 50% size reduction | Measure delegate support and output differences; the estimate is not a crack-model result. |
| Dynamic-range quantization | Quantizes selected values without a full representative-data integer pipeline | Up to 75% size reduction | Latency benefit depends on the hardware path and operator coverage. |
| Post-training integer quantization | Uses integer representations with calibration data | Up to 75% size reduction | Use representative inspection images and verify thin-crack recall, thresholds and accelerator support. |
| Quantization-aware training | Simulates quantization effects during training | Up to 75% size reduction | Requires a training change; it can recover accuracy but is not a substitute for target-device validation. |
| Pruning | Removes or zeros selected weights | The cited implementation does not change on-disk size or runtime latency, although pruned models can compress more effectively. | Do not assume sparsity accelerates inference unless the target engine exploits it. |
The percentages are general estimates from the model-optimization guidance, not measurements for your model or board. Size reduction can lower storage and memory pressure without improving end-to-end latency if preprocessing, memory movement or unsupported layers dominate.
Validate segmentation quality after conversion
Use an identical image list and deterministic settings for baseline and edge runs. For each image, compare the edge output with the reference output and, where available, the ground-truth mask.
Inspect more than a single score
- Intersection-over-union: shows overlap but can hide a missed hairline crack when the affected area is small.
- Precision and false-positive area: reveal whether texture, joints or lighting are being labeled as cracks.
- Recall and missed-crack rate: show whether quantization or resizing removes weak or narrow defects.
- Connected-component behavior: checks whether one crack is fragmented or unrelated regions merge.
- Boundary quality: matters when width, length or propagation measurements are downstream decisions.
- Operational decision: measure the final pass/fail or alarm result, not only pixel metrics.
Use failure-focused image groups
Report results separately for bright and dark surfaces, glossy areas, shadows, oblique cracks, very narrow cracks, long cracks, damaged backgrounds and frames with no cracks. A single aggregate score can improve while a safety-critical subgroup degrades.
Investigate numerical differences
When a converted mask differs, save intermediate tensors from both paths. Compare preprocessing output first, then each major layer or block, then the final logits. Large differences at the first tensor usually indicate image conversion or normalization; differences that appear later may indicate an unsupported operator, precision change or different padding rule.
Benchmark the complete pipeline on the target
Run measurements on the actual production device, camera and enclosure. Report cold-start and steady-state behavior separately where relevant.
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| Measurement | How to record it |
|---|---|
| Capture latency | Timestamp frame request and arrival; include transport and buffering. |
| Preprocessing | Measure decode, crop, resize, color conversion, normalization and tensor upload. |
| Inference | Measure accelerator execution independently from data transfers when the API allows it. |
| Postprocessing | Include thresholding, morphology, connected components, overlays and measurements. |
| End-to-end latency | Measure camera exposure or frame arrival through the final decision. |
| Throughput | Report sustained frames per second, queue depth and dropped frames, not only a short burst. |
| Memory | Record peak RAM, accelerator memory, buffers and leaks during long runs. |
| Thermal and power | Measure under expected ambient temperature and enclosure conditions after the system reaches steady state. |
| Reliability | Exercise camera disconnects, malformed frames, process restarts, storage-full conditions and power cycles. |
Keep model inference time separate from camera-to-result time in every report. This exposes whether the next optimization belongs in the model, the image path or the application.
Treat the camera and optics as part of the model system
An edge board cannot compensate for an unsuitable image. Match the sensor, lens, working distance, field of view, interface, exposure, frame rate and lighting to the inspection environment.
- Resolution: ensure the smallest required crack occupies enough pixels after the planned crop and resize.
- Optics: choose focal length and focus range for the working distance; avoid relying on digital enlargement to recover detail.
- Lighting: control reflections and shadows, and test the full range of production illumination.
- Interface: verify that the board can sustain the camera’s resolution and frame rate without hidden conversion or buffering.
- Timing: account for exposure, rolling shutter, motion blur and trigger synchronization.
- Mounting: lock camera alignment and distance so the validation distribution remains stable.
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Version every dependency
Store checksums and versions for the model, runtime, accelerator driver, operating system, preprocessing code, postprocessing code, camera firmware and calibration data. A model file alone is not a reproducible deployment.
Make startup and failure behavior explicit
At startup, verify that the expected camera is present, the model loads, the input shape matches configuration and the accelerator initializes. On a camera disconnect, fail safely, log the condition and retry according to a bounded policy. Do not silently process stale frames.
Plan updates and rollback
Deploy an immutable versioned bundle, validate it on a representative device, and retain the last known-good bundle. If a new runtime or driver changes operator support or numerical output, rollback must not require rebuilding the image in the field.
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Monitor drift
Retain representative samples subject to your privacy and retention rules. Review lighting, materials, camera alignment, focus and crack appearance over time. A segmentation model can remain unchanged while its input distribution shifts enough to invalidate the original acceptance report.
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Troubleshooting common deployment failures
The converter rejects an operator
Check whether the operator has a supported equivalent, whether a newer export path exists, or whether the layer can be replaced before export. If a custom operator is unavoidable, confirm that the target runtime supports a plugin or delegate implementation. Do not remove the layer merely to complete conversion without revalidating masks.
The model runs but masks are wrong
Compare the first input tensor against the baseline. Check color order, normalization, layout, resize and padding before investigating the network. Then verify output class order, sigmoid or softmax handling, thresholding and any mask upsampling.
Accelerated inference is slower than CPU inference
Inspect graph partitioning, host-device copies, synchronization and first-run compilation. A small model or a graph with unsupported operations may not amortize accelerator overhead. Benchmark steady state and end-to-end latency rather than assuming the accelerator is faster.
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Check representative calibration images against the production distribution, then compare per-subgroup recall and false-positive area. Try a less aggressive precision mode or quantization-aware training if the target supports it. Keep the floating-point artifact as a diagnostic and rollback option.
Throughput falls after sustained operation
Measure temperature, clock frequency, power mode, memory pressure and queue growth. Thermal throttling, buffer leaks or a camera driver that accumulates frames can make a short benchmark misleading.
How to interpret published FPS examples
Published numbers are only meaningful with their model, input dimensions, device and optimization details. For example, the YolactEdge project reports up to 30.8 FPS on a Jetson AGX Xavier with a ResNet-101 backbone and 550×550 images, using TensorRT optimization and temporal feature warping. That is an instance-segmentation result for a specified architecture and platform; it is not a prediction for a crack-segmentation model.
Likewise, an older Jetson camera-processing example based on ONNX Runtime 1.4 and JetPack 4.4, released in the July 2020 timeframe, is useful for understanding the capture-to-inference architecture but should not be treated as a current software baseline. Verify present-day package, driver and runtime compatibility before deployment.
A practical acceptance checklist
- Target board, operating system, accelerator, power and thermal limits are documented.
- Camera interface, optics, lighting, resolution and frame-rate requirements are fixed.
- Original-model outputs and reference metrics are archived on a representative validation set.
- Converted floating-point outputs match the baseline within a project-defined tolerance.
- Every quantization or graph optimization has a separate mask-quality report.
- Input and output tensor contracts are tested automatically.
- Capture, preprocessing, inference and postprocessing timings are measured independently.
- Sustained temperature, power, memory and dropped-frame behavior meet requirements.
- Camera disconnect, malformed input, restart and rollback paths have been exercised.
- Model, runtime, driver and configuration versions are packaged and checksummed.
The defensible deployment decision is the smallest, coolest and most maintainable configuration that meets your measured mask-quality and camera-to-result requirements. Select the runtime and hardware only after those tests identify the actual bottleneck.
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