AI Defect Inspection

Spot damage, scratches, cracks, dents, or missing pieces as small as 0.01% of an image. Detect unforeseeable defects that differ in shape from previously collected examples.

V7 allows developers to turn images and video of your defects into state of the art vision AI with semi-automated tagging, model training, and inference deployment.

Get started today by automating the detection of anomalies in your production line or manufacturing process, and bring the growing value of AI to your supply chain.

Hover over the image below to see what the AI identifies

See AI Defect Inspection in Real Time

More examples of defect inspection developed on V7

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What can you identify

No two defects are alike, but they generally fall within broad categories of appearances:

Scratches

Discoloration

Cracks

Holes

Dents

Unknown anomalies / missing pieces

ML-Ops defines your accuracy. We help you grow it to 99.9%.

AI performance shouldn't stop at the first training session.
Sync your product with a dataset and continually improve your AI performance by turning its output into new ground truth. Continual learning will allow your models to surpass 95% accuracy barriers and reach 99% and above as more data from your use case or product is supervised by humans, and learnt by your AI.

Your product

Fast labeling on V7 Darwin

Newly collected images or video

Training & active learning

Zero to State of the Art

Bring AI to life from day 1. With the fast pace of deep learning's state of the art, V7 ensures that on-platform models are the most accurate on the market. By continually updating our architecture, and leveraging an extensively pre-trained backbone, your models will always maximize precision and recall.

Instance Segmentation (Object Detection + Segmentation) benchmark:

V7 Neurons (VoVNet)

Average Precision

46.0

Inference speed (FPS)

Mask R-CNN (Detectron2)

Average Precision

41.0

Inference speed (FPS)

YOLACT550++ (YOLO)

Average Precision

34.6

Inference speed (FPS)

Model backbone benchmark:

V7 Neurons (Ours)

Average Precision - Detection

43.8

AP - Masking

39.3

ResNet-50

Average Precision - Detection

41.0

AP - Masking

37.2

MobileNetV2

Average Precision - Detection

33.0

Benchmarks were performed on the MS-COCO dataset. COCO is largely composed of large, frame-filling objects that are easy to detect by modern neural network architectures. V7 Neurons outperformed Mask-RCNN primarily on the detection of small and uncommon objects, which matter most in industry cases.