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Deploying a crack-segmentation model at the camera is a conversion-and-validation project, not simply a matter of copying a model onto a small computer. Choose a device and runtime that support the model’s operators and an available accelerator, establish a reference result, convert the model, optimize it in measured stages, and test the complete camera-to-mask pipeline under sustained operating conditions.
This guide assumes you already have a trained model but have not specified its framework, input dimensions, target board, frame-rate requirement, power budget, or inspection environment. Those variables determine the correct export path and hardware. The procedures below therefore use a device-neutral plan, with LiteRT, ONNX Runtime on Jetson-class hardware, and TensorRT as example runtime paths rather than universal recommendations.
Define what “deployed” must achieve
A useful edge deployment produces a trustworthy crack mask within the latency, power, thermal, and reliability limits of the inspection system. Model inference is only one part of that result. A practical pipeline normally contains:
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- Frame acquisition from the camera and handling of disconnects or malformed frames.
- Preprocessing such as resizing, color conversion, normalization, and tensor layout changes.
- Model execution on the CPU, GPU, NPU, or an accelerator delegate.
- Postprocessing that converts output tensors into a mask, thresholded regions, measurements, or an inspection decision.
- Buffering, display or storage, logging, and communication with the rest of the machine.
Before selecting a runtime, write down the outcome that matters. A system that must stop a production line after a missed crack has different acceptance criteria from one that merely highlights areas for a human operator. No cited deployment reference supplies a universal IoU, precision, recall, or missed-crack threshold; the inspection owner must set those limits from the consequences of errors.
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Record the target before choosing hardware or runtime
Use a short target specification. If any item is unknown, treat it as an open engineering decision rather than assuming a default.
| Item | What to record | Why it changes the deployment |
|---|---|---|
| Model origin | Framework, export method, input and output tensors, custom layers, and opset or equivalent version | Determines which converters and operators are available |
| Input | Required width and height, color order, normalization, tensor layout, and aspect-ratio policy | Preprocessing mismatches can invalidate an otherwise correct model |
| Output | Logits or probabilities, class channels, mask dimensions, thresholds, and any decoder or resize operation | Postprocessing must reproduce the model’s intended meaning |
| Device | Board, operating system, CPU, GPU or NPU, memory, camera interface, and storage | Controls runtime support, memory headroom, and data movement |
| Operating envelope | Acceptable camera-to-result latency, throughput, power limit, ambient temperature, and whether operation is offline | Peak benchmark numbers are not enough for a continuously running unit |
| Inspection scene | Surface materials, crack widths, working distance, lighting variation, motion, and nuisance patterns | These conditions define both the camera design and the validation set |
Pick the runtime only after this sheet exists. Google presents LiteRT as an on-device framework with conversion, quantization, and CPU, GPU, and NPU execution, including image-segmentation examples. NVIDIA documents ONNX Runtime camera processing on Jetson-class systems and describes TensorRT as an inference optimizer and runtime for NVIDIA platforms. Those capabilities do not prove that an unspecified model and board are compatible; operator support and accelerator integration must be checked for the exact pair.
Establish an unoptimized reference
Do not quantize or fuse operations before you know what the original model produces. The reference is your basis for deciding whether a conversion or optimization changed the inspection result.
- Run the known-good model in its original environment. Use the same preprocessing and postprocessing that produced the model’s training or evaluation results.
- Freeze a representative validation set. Include the intended surfaces, narrow and wide cracks, clean areas, expected lighting, blur, glare, texture, stains, joints, and other patterns that can resemble cracks. Keep the images and reference masks under version control.
- Save outputs, not just a single score. Retain predicted masks, probability maps where useful, and the settings used to threshold or resize them.
- Choose task measures. Intersection-over-union, precision, recall, missed-crack rate, and false-positive area are common candidates. Select the measures that reflect the cost of an error in your inspection process.
- Record timing in the reference environment. Note input dimensions, batch size, warm-up behavior, and whether the measurement covers only inference or the whole application.
This baseline is an application-level contract. A converted model does not need identical floating-point values in every tensor if the final crack decisions remain within the project’s accepted limits, but you must measure the actual masks and inspection outputs before proceeding.
Choose and execute a conversion path
LiteRT for supported mobile and embedded targets
LiteRT supports conversion paths from multiple machine-learning frameworks and execution on CPU, GPU, or NPU through supported delegates. Its current overview recommends the CompiledModel API for developers seeking current performance while retaining the older Interpreter for compatibility. Confirm the live API and delegate coverage for your operating system and board before committing.
Use the exporter documented for the model’s originating framework. After conversion, inspect the generated model for input shape, data type, tensor layout, and output names. Run the artifact on the frozen validation images with exactly the same resize, color-order, normalization, mask decoding, and thresholding rules used by the reference.
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ONNX Runtime on Jetson-class devices
NVIDIA’s Jetson example shows a camera frame being passed through ONNX Runtime with CUDA and cuDNN acceleration, with optional TensorRT support when ONNX Runtime is built from source. The article describes ONNX Runtime 1.4 and JetPack 4.4 and is dated to 2020, so treat it as an architecture example, not a current package recommendation. Verify present-day JetPack, CUDA, cuDNN, ONNX Runtime, and TensorRT compatibility before building.
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TensorRT on NVIDIA hardware
TensorRT can optimize and execute supported networks on NVIDIA platforms. Build an engine for the precise input shape, precision mode, and device that will be deployed, then compare its masks with the reference model. Unsupported layers, dynamic-shape profiles, plugins, and postprocessing outside the engine can all affect both compatibility and total latency. Keep the engine-building environment and settings recorded so that it can be reproduced after a software update.
Conversion checks that apply to every runtime
- Shape: Confirm whether the runtime expects a fixed image size, a dynamic shape, or a batch dimension that must be set explicitly.
- Color and layout: Verify RGB versus another channel order and NCHW versus NHWC or the equivalent layout.
- Scaling: Match the model’s normalization constants, numeric range, and data type.
- Output interpretation: Confirm whether outputs are logits, probabilities, class indices, or encoded masks, and reproduce the required resize and threshold operations.
- Operators: Identify unsupported or custom operations before deployment. A converter may reject them, replace them, or leave part of the graph on a different processor.
- Parity: Run the converted artifact on the same validation images and compare masks and task metrics before enabling quantization.
Optimize only after conversion parity is understood
Optimization is a sequence of experiments. Change one variable at a time, preserve the preceding artifact, and rerun both quality and performance tests after each change.
Quantization choices
TensorFlow Lite’s model-optimization guidance describes post-training float16, dynamic-range, and integer quantization, as well as quantization-aware training (QAT). Its estimates below are toolkit guidance, not measurements for a crack model or a particular board.
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| Technique | Guidance estimate | What to verify |
|---|---|---|
| Post-training float16 | Up to 50% model-size reduction | Whether the target accelerator supports the chosen precision and whether mask quality changes |
| Dynamic-range quantization | Up to 75% model-size reduction | Runtime support, activation handling, and false positives or missed narrow cracks |
| Post-training integer quantization | Up to 75% model-size reduction | Representative calibration data, integer I/O requirements, and accuracy on the inspection set |
| Quantization-aware training | Up to 75% model-size reduction | Retraining or fine-tuning effort and whether the resulting model meets the same mask criteria |
Quantization reduces numerical precision and can alter accuracy. For crack inspection, a small change in a thin boundary may matter more than a similar change in a large, obvious region, so inspect per-image masks and false-positive area rather than relying only on an aggregate score. Use calibration images that represent the camera’s real exposure, materials, and crack widths.
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Pruning and clustering
The same guidance characterizes pruning and clustering primarily as compression techniques. In the described implementation, pruning does not change on-disk size or runtime latency by itself, although pruned models can compress more effectively. Do not assume that a sparse or clustered model will run faster on your board unless the selected runtime and accelerator explicitly exploit that structure and a target benchmark confirms it.
When an optimization fails
- If quality drops, compare the first artifact that changed with the previous artifact on identical images. Check whether the error is concentrated in narrow cracks, high-glare frames, a particular material, or a specific output channel.
- If latency does not improve, profile preprocessing, memory copies, CPU fallbacks, and postprocessing. A smaller model can leave the dominant bottleneck untouched.
- If the model no longer loads, restore the last known-good artifact and inspect unsupported operators, tensor types, shape profiles, and runtime-version changes.
Treat the camera as part of the model system
An edge board cannot compensate for an unsuitable image. Select the sensor and interface together with the optics and installation:
- Resolution: Preserve enough pixels across the smallest crack that must be detected. The model’s input size is not automatically the camera’s useful resolution; downsampling can erase evidence.
- Optics and distance: Fix the working distance, field of view, focus, and depth of field for the surface geometry.
- Lighting: Control illumination, reflections, shadows, and exposure changes that can turn texture into false positives or hide shallow cracks.
- Frame rate and motion: Match acquisition rate and shutter behavior to conveyor or inspection movement. A high model FPS is irrelevant if the camera cannot deliver usable frames.
- Interface and bandwidth: Ensure the board can ingest the selected camera format without consuming the memory or processor budget needed for inference.
- Mounting and maintenance: Keep alignment, focus, and lighting stable, and define how contamination or vibration will be detected.
A USB machine-vision camera is one possible input category, not a universal recommendation. The correct choice depends on the device interface and the physical inspection conditions. NVIDIA’s Jetson example explicitly reads camera frames before inference, illustrating why camera capture belongs in the deployment design.
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Measure on the actual device, with the intended camera and software image. Separate model inference from camera-to-result time so an apparently fast network does not conceal a slow pipeline.
- Define timing boundaries. Timestamp frame acquisition, preprocessing, inference, postprocessing, and final result delivery. State whether queue wait and display or network transmission are included.
- Measure cold start. Record model-loading time, engine compilation or delegate initialization, camera startup, and the first usable result if the unit may restart or run intermittently.
- Measure steady state. After warm-up, collect latency distributions rather than only an average. Include minimum, typical, and worst observed values over a sustained run.
- Measure throughput and drops. Record camera rate, processed frames per second, queue depth, dropped frames, and whether processing is synchronous or asynchronous.
- Measure resources. Track peak and steady-state memory, CPU/GPU/NPU utilization, temperature, and power under the expected workload. Thermal throttling can make a short benchmark misleading.
- Exercise difficult inputs. Test dark, bright, blurred, partially occluded, empty, oversized, and malformed frames, plus camera disconnect and reconnection.
- Repeat after packaging. The production service, logging, watchdog, and communication layers can change timings compared with a laboratory script.
Report the input dimensions, model artifact, precision, runtime and accelerator versions, device configuration, and measurement boundaries with every benchmark. Do not rank devices by headline FPS from unrelated models.
Validate mask quality under inspection conditions
Build a test matrix
Partition validation images by factors that can change the decision: surface type, crack width and orientation, illumination, distance, focus, motion, contamination, and nuisance patterns. Keep a held-out set for final acceptance so repeated tuning does not make the reported result optimistic.
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Review more than aggregate scores
For each important category, inspect overlays of predicted and reference masks. Record missed cracks, extra regions, boundary quality, and whether a small false-positive area triggers an unacceptable inspection decision. If the application measures crack length, width, or connected components, validate those derived measurements after postprocessing, not just the raw segmentation tensor.
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Set acceptance rules with the inspection owner
Define the minimum acceptable task metrics, maximum latency, allowable frame-drop behavior, and restart or camera-failure response before production release. The cited framework documentation does not establish these thresholds for your process; they must follow from the cost of missed and false detections.
Package a reproducible edge application
Ship the model and the code that gives it meaning as one versioned artifact. Include:
- The converted model or accelerator engine and its checksum.
- Preprocessing and postprocessing code, including resize, normalization, thresholds, and output decoding.
- Runtime, accelerator, operating-system, and driver versions known to work together.
- Camera configuration, image dimensions, exposure or lighting settings that are part of the validated setup, and device-specific configuration.
- A small smoke-test set with expected masks or decision summaries.
- Resource limits, logging settings, health checks, and a documented rollback artifact.
Pin mutually compatible software versions rather than allowing an unattended package update to replace a runtime or driver. On startup, verify that the model loads, the camera is present, tensor shapes match, and a known smoke-test frame produces an expected result. For an unattended unit, define behavior for camera loss, corrupt input, out-of-memory errors, thermal alarms, and repeated inference failures.
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Accuracy can drift even when the model file is unchanged. Watch for altered lighting, material changes, camera movement, focus loss, new surface textures, and changes in crack appearance. Retain representative samples according to your privacy and retention requirements, review unusual masks, and periodically rerun the acceptance set after maintenance or a physical installation change.
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How to compare runtime and platform candidates
Use the same model, input, validation images, and timing boundaries when comparing options. A practical decision matrix is:
| Criterion | Questions to answer |
|---|---|
| Framework and operator compatibility | Can the model be exported without unsupported or custom operations, and do all required operations run on the intended accelerator? |
| Acceleration | Which CPU, GPU, NPU, CUDA, cuDNN, delegate, or TensorRT path is available, and are there CPU fallbacks? |
| Precision support | Can the device execute the selected float or integer precision, and does quantization preserve the required mask quality? |
| End-to-end performance | What are camera-to-result latency, sustained throughput, frame drops, and cold-start time on this exact model? |
| Memory, power, and thermal headroom | Does the complete application remain within limits during sustained operation, not just during a short test? |
| Camera connectivity | Can the board ingest the required resolution and frame rate while leaving resources for inference? |
| Maintenance | Can the software image, engine, runtime, and rollback process be reproduced and supported over the product’s life? |
LiteRT is a candidate when its conversion path and target delegates support your model. ONNX Runtime can fit a Jetson pipeline when the required execution providers and operators are available. TensorRT is a candidate for NVIDIA hardware when its engine-building constraints and supported operations match the network. Select among them using measured compatibility and pipeline results, not brand familiarity or a published FPS figure.
Interpreting published performance examples
YolactEdge reports up to 30.8 FPS on a Jetson AGX Xavier with a ResNet-101 backbone and 550×550 images. The project attributes its design to TensorRT optimization and temporal feature warping. This is an instance-segmentation result for a specified architecture, resolution, device, and optimization; it is not a crack-segmentation benchmark and does not predict the speed of another model.
The NVIDIA ONNX Runtime Jetson article’s references to ONNX Runtime 1.4 and JetPack 4.4 describe its 2020 software context. Current packages and compatibility must be verified before implementation. Likewise, TensorFlow Lite’s “up to” size-reduction estimates describe optimization techniques in general, not measured savings or accuracy for this deployment.
A device-neutral implementation loop
The following pseudocode shows the order of operations without assuming a framework or board:
load_versioned_model_and_runtime()open_camera_and_verify_format()while service_is_running:frame = capture_frame()validate_frame(frame)tensor = preprocess(frame, documented_parameters)raw_output = runtime.run(tensor)mask = postprocess(raw_output, documented_parameters)emit_result(mask)record_timing_and_health()
Instrument each line separately during testing. In production, make the model, preprocessing parameters, postprocessing parameters, and runtime versions visible in logs or a diagnostic report so an output can be traced to a known artifact.
Quick Recap
Common failure modes and fixes
| Symptom | Likely cause | Focused response |
|---|---|---|
| Masks are shifted or colors look wrong | Resize policy, channel order, tensor layout, or normalization differs from the reference | Run one image through both paths and compare each preprocessing stage |
| Model loads but is unexpectedly slow | Unsupported operations or postprocessing are falling back to the CPU; memory copies dominate | Inspect execution-provider placement and profile preprocessing, inference, and postprocessing separately |
| Quantized model misses thin cracks | Precision or calibration changed boundary-sensitive outputs | Compare per-category masks, use representative calibration data, and revert or try another precision path |
| Short benchmark is fast but long runs slow down | Thermal throttling, memory pressure, or queue growth | Run a sustained test while logging temperature, power, memory, and frame drops |
| Results fail after a software update | Runtime, driver, delegate, engine, or camera-version incompatibility | Restore the pinned artifact, rerun the smoke test, and promote updates only after full validation |
| Intermittent false positives in production | Lighting, focus, alignment, or surface conditions differ from the validation set | Review captured examples, correct the physical setup where possible, and expand the validation matrix |
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