
Manufacturing Defect Inspection
Scratches, weld faults, missing parts and surface defects caught on the line.
AiVisionBot is AI computer vision software by VYROX that puts cameras to work on the jobs that make money, not just the ones that watch doors: it finds defects on the line, grades produce, counts trucks and livestock, measures stockpiles and checks PPE, then raises an alert or switches a relay through IoTServa in seconds.
Ranked by measurable customer value, commercial demand, technical maturity and how much of the vision engine they share. Pick your industry to see what fits.

Scratches, weld faults, missing parts and surface defects caught on the line.

Pinholes, tears, stains and thin spots spotted on every former at line speed.

Truck trips, loading, payload fullness and plant movement tracked.

Aggregate and material pile volumes from drone or fixed cameras.

Helmets, vests, restricted machinery zones and forklift proximity.

Ripe, unripe, overripe and empty bunches graded at the mill ramp.

Parcel counting, dimensioning and damage spotted on the conveyor.

Unsafe lifting, bending and reaching scored from body pose.

Label presence, cap, fill level and lot code read on every unit.

Hairnets, masks, gloves and handwashing checks in food prep.

Bird and animal counts, activity, crowding and mortality signs.

Fish counts, size estimation and feeding behaviour underwater.

Road cracks, potholes and structural damage from vehicles or drones.

Solar panel faults, substation and line defects.

Crop health, pest signs, tree and fruit counting from drones.

Empty shelves, misplaced products and planogram checks.

Container codes, crane moves and yard turnaround measured.

Visible smoke and flame flagged in yards and warehouses.

Falls and long immobility in care homes and wards.
Leaks, corrosion, overheating and abnormal visible conditions.
Conveyor activity, production counts and material flow.
Dimensions, clearances and alignment, with calibrated cameras.
Milestones and installed items compared with the plan over time.
Picking, placing and sorting guided by camera.
Ripeness, size, defects and packaging faults graded.
Materials classified to guide sorting lines.
Invoices, delivery orders and forms read and checked.
Vehicle and property damage classified from photos.
Radiology and pathology assistance for clinicians.
Player tracking, pose and exercise form analysis.
Our first production deployment watches a granite quarry: a custom-trained YOLO model on Ultralytics Enterprise detects each dump truck, tracks it through the weighbridge and loading bay, and estimates how full the tray is from its volume.
Every use case above is a combination of the same twelve vision capabilities. We build each one once and reuse it everywhere, so a new use case is configuration and training, not a new product.
Sorts a whole frame into a category.
Normal / DefectiveFinds and labels every object in view.
Person, Car, HelmetKeeps one ID per object across frames.
Truck #12 in, outMarks the exact pixels of each object.
Crack area 4.2 cm²Reads plates, container IDs and labels.
Plate, serial, invoiceCounts items, animals or trips through a line or zone.
Fish per cageFinds body joints and posture.
Worker bendingUnderstands movement over time.
Walking, FallingLearns normal, flags anything else.
Unexpected objectEstimates distance and 3D shape.
Forklift 1.8 m awaySize, area and volume from calibrated views.
Stockpile volumeDescribe or search footage in plain words.
"Bunches with long stalks"A fast detector looks at every frame. The heavier vision-language model is only called when something is worth explaining, so you get intelligence without paying GPU prices on every pixel.
Add line, drone or IP cameras. Draw zones, lines and schedules.
Every frame is scanned for the objects your use case needs.
Objects keep their ID so we count once and measure dwell time.
Only flagged clips get a vision-language model to explain them.
Rules turn detections into events: zone, time, count, duration.
Notify, reject a part, stop a conveyor, log a report, all audited.
A small fanless edge box beside your NVR runs the models locally. Only events, snapshots and counts go to the cloud, which saves bandwidth, keeps working when the internet drops and keeps footage under your control.

We are not married to one model. Each layer uses the tool that benchmarks best for your footage, with licences checked before anything ships.
| Layer | Tools We Use | Best For |
|---|---|---|
| Object Detection | Ultralytics YOLO26, RF-DETR, RTMDet | Fast, accurate boxes on every frame |
| Open-Vocabulary | Grounding DINO, YOLOE, Florence-2 | Find new objects described in words |
| Segmentation | Meta SAM 2, SAM 3.1, MMSegmentation | Pixel masks, cracks, stockpiles, labelling |
| Tracking | ByteTrack, BoT-SORT, OC-SORT | Counting, dwell time, identity through occlusion |
| Vision Language | OM AI VLX-Seek 1.5, Qwen3-VL | Grounded queries and plain-language descriptions |
| Pose and Action | MMPose, RTMPose, MMAction2 | Falls, ergonomics, unsafe lifting |
| OCR | PaddleOCR, Fast-ALPR, DocTR | Lot codes, labels, container codes, documents |
| Anomaly | Anomalib (PatchCore, EfficientAD) | Defects with few bad samples |
| Embeddings | CLIP, SigLIP, DINOv3, Qdrant | Search by image or meaning |
| Inference | TensorRT, OpenVINO, ONNX Runtime, DeepStream | Many cameras per box at low latency |
| Data and Training | CVAT, FiftyOne, PyTorch, MLflow | Labelling, error review, retraining on your site |
AI vision is powerful and still imperfect. These are the lines we will not blur in a sales meeting.
Smoke and flame detection is an early warning that sits beside certified detectors, never instead of them.
Millimetre claims need calibrated cameras and an uncertainty study. A single photo gives an estimate, not a spec.
Fine defects need the right camera, lens and light. We check optics on site before promising an accuracy number.
Medical imaging features support clinicians and are not a regulated diagnosis. We tell you which items are pilots.
Usually not. Any IP camera with an RTSP or ONVIF stream at 1080p works for most use cases. Fine defects, underwater and long-range views need the right lens, lighting and mounting, which we check during the site assessment.
It does not have to. Inference can run on an edge box on site, sending only events, snapshots and counts to the cloud. Cloud processing is optional.
It depends on the camera angle, lighting and how close your site is to the training data. We measure precision and recall on your own footage during the pilot and share the numbers before you commit.
We pick per use case: YOLO-family and RF-DETR detectors, ByteTrack or BoT-SORT tracking, SAM segmentation, PaddleOCR, Anomalib for defects, MMPose for posture and vision-language models for natural-language search, fine-tuned on your footage where accuracy matters.
AiVisionBot runs on the IoTServa platform, so a vision event can trigger anything IoTServa controls: email, WhatsApp or app alerts, relays, reject gates, conveyors, MQTT, Modbus and the Open API.
No. Visual smoke and fire detection is an early-warning layer that adds to, never replaces, certified detection and alarm systems.
Send a short clip from your site and pick a use case. We run it through AiVisionBot and walk you through the detections, the accuracy and the cost before you buy anything.