Infrared Image Algorithm Development and Device Integration
For equipment manufacturers, system integrators, industrial enterprises and scientific research teams, it provides sample evaluation, algorithm development, SDK/API, edge deployment and verification design around NIR, SWIR, MWIR, LWIR, radiometric temperature matrix, dual-light data and active thermal imaging sequences.
What problem does this service solve?
Infrared is not a single image type. The 8-bit pseudo-color image can be used for target and relative hot beam profile analysis, but it cannot directly replace the radiometric temperature matrix; when temperature, heat flow, defect depth or material composition needs to be reported, corresponding calibration, physical conditions and independent true values must be supplemented.
First distinguish the infrared data type
The spectral band, detector, lens, filter and signal source determine the algorithm that can be developed. The project plan is based on the spectral response, imaging parameters and interface data of the target device.
| Imaging type | Common project scope | main signal source | Typical work | Main limitations |
|---|---|---|---|---|
| Near infrared NIR | About 0.75–1.0/1.4 μm | Reflected light, active fill light | Night vision, iris, veins, vegetation, visible light complementary | Not a temperature map; affected by light source, surface reflection, and sunlight |
| Short wave infrared SWIR | About 0.9/1.0–2.5 μm | Reflection, transmission, partial high-temperature radiation | Moisture content difference, material sorting, silicon wafer perspective, smoke environment observation | Wavebands and material absorption peaks must match; ordinary broadband graphs cannot replace spectral measurements |
| Medium wave infrared MWIR | About 3–5 μm | Thermal radiation, specific gas absorption | High temperature targets, long-distance thermal targets, optical gas imaging | Cooled detectors or narrow-band filtering are often required; high cost and calibration requirements |
| Long wave infrared LWIR | About 8–14 μm | Thermal radiation of normal temperature objects | Thermal imaging, equipment inspection, human/animal sensing, building thermal engineering | Metal reflection, glass opacity, emissivity and environmental reflection have obvious effects |
| Infrared multispectral/hyperspectral | Multiple discrete or continuous bands | Reflection, absorption or emission spectra | Material classification, water content/composition regression, remote sensing inversion | Requires darkfield/whiteboard/spectral calibration, inter-instrument migration and batch validation |
| Dual light/multi-sensor | RGB+NIR/SWIR/MWIR/LWIR, can superimpose depth, radar or lidar | Multiple sources complement each other | All-weather detection, object recognition, spatial positioning, result review | Time synchronization, parallax, focal length, field of view and occlusion differences must be dealt with |
Infrared algorithm development capability classification
The eight categories of capabilities each cover 20 algorithms or engineering workflows, and can be implemented based on a combination of project input, output, deployment platform and acceptance indicators.
Image linking, correction and enhancement
Covers 20 algorithms and workflows such as infrared original frame bit depth, endian and row step analysis, temperature matrix, radiation frame and display frame consistency check, gain file, range file and integration time identification.
View classified services →C02 · I021–I040Radiation thermometry, thermal field measurement and uncertainty
Covers 20 algorithms and workflows including DN to in-band radiance conversion, radiance to apparent temperature conversion, emissivity estimation, configuration and regionalization compensation.
View classified services →C03 · I041–I060Geometric, cross-spectral registration and multi-sensor fusion
Covers 20 algorithms and workflows, including infrared camera internal distortion calibration, RGB-IR external participation in rigid body relationship calibration, and planar homography cross-spectral registration.
View classified services →C04 · I061–I080Detection, segmentation, identification and anomaly analysis
Covers 20 algorithms and workflows such as global/adaptive thermal threshold segmentation, local background difference thermal anomaly, and multi-scale top-hat small hotspot detection.
View classified services →C05 · I081–I100Infrared Video, Tracking, Behavior and Events
Covers 20 algorithms and workflows such as dual-frame/three-frame differential motion detection, MOG/adaptive background modeling, sparse/dense infrared optical flow, etc.
View classified services →C06 · I101–I120Active thermal imaging and thermal non-destructive testing
Covers 20 algorithms and workflows such as thermal response curve extraction and baseline subtraction, heating and cooling rates and time constants, and original thermal contrast of pulse thermography.
View classified services →C07 · I121–I140NIR/SWIR, multispectral, hyperspectral and remote sensing
Covers 20 algorithms and workflows including dark field, whiteboard and reflectance calibration, spectral bad lines, bad bands and noise band removal, smile/keystone spectral geometry correction, etc.
View classified services →C08 · I141–I160Data, models, deployment and validation
Covers 20 algorithms and workflows such as 8/12/14/16-bit unified normalization and reversible mapping, data enhancement that maintains the physical meaning of temperature, and inter-sensor domain adaptation.
View classified services →Input data level determines deliverables
| level | Available inputs | Applicable jobs | Restrictions apply |
|---|---|---|---|
| L0 | JPG/PNG screenshots, pseudo-color images, and color-coded images | object detection, relative hotspot, area and event analysis; restricted image reading is possible when color mark exists | Raw pixel temperature, absolute temperature accuracy, full dynamic range |
| L1 | Raw 12/14/16 bit DN or linear grayscale without full radiometric parameters | NUC, dead pixels, relative thermal anomalies, timing changes, training detection model | Celsius temperature, comparable temperature difference across devices |
| L2 | radiometric temperature matrix and camera parameters | ROI Temperature, isotherms, temperature differences, object-level temperatures and trends | Verified target surface temperature without compensation for emissivity, reflected temperature, distance, atmosphere, and window |
| L3 | Infrared video/temperature sequences with reliable timestamps | Tracking, thermal process curves, trends, change points, flame/plume motion | Frequency, phase or response time when frame time is unknown |
| L4 | Synchronize RGB-IR, IR-Depth, IR-Radar or multi-camera data | Registration, fusion, cross-modal detection, 3D heatmaps and object association | Pixel-by-pixel temperature mapping under uncalibrated parallax conditions |
| L5 | Active thermal excitation sequence, spectral cube, blackbody/whiteboard/dark field and physical truth | Thermal non-destructive testing, spectral material analysis, physical property/defect parameter inversion | Internal defect depth and material composition without stimulus, reference sample or true values |
Please indicate current data level when enquiring. Visual identification and relative change analysis can use display plots or raw DN; absolute temperature measurements require radiometric parameters, calibration data and reference measurements.
20 technical routes for splitting project scope
Infrared image link and quality correction
Minimum input: L1; main output: available frames, bad pixel map, NUC parameters, quality status.
View directions and input conditions →T02Relative thermal anomalies compared to reference areas
Minimum input: L0/L1; main output: hot spot/cold spot, relative temperature difference proxy, abnormal area.
View directions and input conditions →T03Radiation Thermometry and Uncertainty
Minimum input: L2; main outputs: temperature matrix, ROI temperatures, compensation parameters, and error budget.
View directions and input conditions →T04Object recognition and temperature correlation
Minimum input: L2 or L4; main output: object, part ID, temperature statistics and alarms.
View directions and input conditions →T05Temperature time series and status trend
Minimum input: L3; main output: rate of change, steady state value, change point, trend and health indicators.
View directions and input conditions →T06Visible-infrared registration and fusion
Minimum input: L4; main output: registration transformation, fusion map, cross-modal features and confidence.
View directions and input conditions →T07Detection and tracking of people, vehicles and animals
Minimum input: L0/L3/L4; main output: categories, boxes/masks, trajectories, counts, and events.
View directions and input conditions →T08Flame, high temperature and heat source analysis
Minimum input: L1 / L2 /
View directions and input conditions →T09Optical gas imaging
Minimum input: dedicated MWIR/LWIR L3; main output: air plume candidates, trajectory, duration; quantification requires special calibration.
View directions and input conditions →T10Active thermal imaging non-destructive testing
Minimum input: L5; main outputs: defect contrast, phase, depth candidate, and POD.
View directions and input conditions →T11Active NIR Imaging and Biometrics
Minimum input: NIR L1/L3; main output: iris/vein/eye/reflective target features.
View directions and input conditions →T12SWIR Differences in water content and materials
Minimum input: SWIR
View directions and input conditions →T13Infrared multi/hyperspectral analysis
Minimum input: L5; main outputs: spectral classes, endmembers, abundances, anomalies, and composition regression.
View directions and input conditions →T14Satellite/UAV infrared remote sensing
Minimum input: L4/L5; main output: surface temperature, index, change zone, and GIS layer.
View directions and input conditions →T15Heat map 3D mapping and robot perception
Minimum input: L4; main output: 3D thermal texture, target coordinates, risk zone and navigation information.
View directions and input conditions →T16Human/Animal Surface Thermal Physiology Research
Minimum input: controlled L2/L3; main output: symmetry, temperature recovery curve and auxiliary indicators.
View directions and input conditions →T17High temperature process and radiation analysis
Minimum input: MWIR/SWIR L2/L3; main output: high temperature field, molten pool/furnace wall status and process parameters.
View directions and input conditions →T18Silicon wafer, photovoltaic EL/PL and infrared luminescence
Minimum input: NIR/SWIR L1/L5; main output: cracks, failure areas, luminescence uniformity and classification.
View directions and input conditions →T19Rules, Tracking and Business Events
Minimum input: L3/L4; main output: invasion, stay, count, linkage candidate and event fragment.
View directions and input conditions →T20Data, model, deployment and acceptance governance
Minimum input: any; main output: version, hierarchical indicators, drift, alarm audit and recurrence data.
View directions and input conditions →Enterprise project implementation path
- Project input confirmationCheck camera, spectral band, bit depth, SDK, environmental conditions, sample and acceptance true value.
- Sample verificationVerify key processing links, applicable conditions and abnormal working conditions, and determine the development scope and acceptance methods.
- Algorithm development and integrationImplement data parsing, algorithms, quality gating, interfaces and target platform deployment.
- Test acceptance and handoverRetest according to the agreed test set and target platform, and hand over the deliverables, test results and applicable conditions.
Agreable deliverables
Delivery mix is determined based on project phase, deployment platform, source code permissions, model licenses, and scope of on-site work.
- Requirements and technical solutions, input and output and interface field definitions
- Algorithm library, model or SDK/API; delivery form is confirmed in the project plan
- Sample program, parameter configuration, error status and calling instructions
- Test set version, indicator definition, stratification results and failed sample records
- Target platform adaptation, resource usage and running delay records
- Version, dependencies, license, reproduction steps and applicable conditions description
Acceptance methods and project boundaries
| Task type | Suggested acceptance metrics |
|---|---|
| Radiation thermometry | MAE, Bias, P95, Repeatability, Drift, Field Uniformity, Different Emissivity/Distance/Environmental Stratification |
| hot/cold spots | Recall, Precision, IoU, center error, minimum detectable area, ΔT layering |
| Object detection | mAP, Recall, false alarms/hour or kilometers, distance/pixel height/weather/occlusion layering |
| Tracking and events | IDF1, ID Switch, trajectory integrity rate, event F1, alarm delay, repeated alarm rate |
| Registration and synchronization | Reprojection error, edge distance P95, time deviation, drift, failure rate |
| Trends and warnings | Discovery lead time, change point latency, false alarms/day, cross-load stability |
| Active thermal NDT | POD, a90/95, SNR, minimum defect, size/depth error, reference block repeatability |
| spectral regression | Set aside batches RMSE/MAE, R², deviation, instrument/temperature/variety migration |
| Spectral classification | Macro-F1, Recall per class, Unknown class rejection, Batch external verification |
| remote sensing | Ground truth RMSE, change area IoU, geolocation error, cloud/invalid pixel quality layer |
| system | End-to-end delay, frame rate, missed frame rate, online rate, restart recovery time, resources and power consumption |
| data governance | Raw data integrity, metadata integrity, hashing, version traceability, permissions and deletion verification |
Acceptance thresholds are written into the project plan after sample evaluation, and test data, reference measurements, hardware, software, parameters and repeated test conditions are fixed.
Frequently Asked Questions for Enterprise Customers
Can I develop only JPG, PNG or pseudo-color images?
Object detection, area extraction, relative hot spots and event analysis can be carried out first. If pixel temperature, absolute temperature accuracy, or cross-device temperature differences need to be reported, a radiometric temperature matrix, camera parameters, and reference ground truth should be provided.
Is it possible to provide a generic IR model or SDK directly?
It is necessary to check the target spectral band, resolution, distance, target number of pixels, working conditions, category and hardware platform first. Common modules can serve as the basis for development, but cannot replace target device and product domain data verification.
How to quote for algorithm projects?
It is usually divided into sample evaluation, special verification, algorithm or model development, SDK/API, target platform deployment, equipment joint debugging and acceptance. Sample coverage, physical calibration, third-party licensing, source code scope, and field conditions all impact workload.
Can it be used for fire protection, medical or equipment shutdown linkage?
Such uses need to complete regulatory applicability, functional safety, failure handling, manual review and on-site acceptance design, and be activated according to the approved linkage plan.
Define the Development Scope with Samples and Device Requirements
Infrared Image Algorithm Development and Device Integration
Please describe the task object, original picture or video, target indicators, device platform, interface and sample situation.
Phone: 13910119357xuzhiyang0928@gmail.com