Automated defect detection
Catch defects the moment they appear. Across recent peer-reviewed studies, YOLO-based inspection reaches 99%+ accuracy and cuts manual inspection costs by up to 94.5%, on steel, PCBs, fabric, solar panels, and welds. Train it on your own defects and deploy it on the line.
Trusted by the world's leading organizations
How Ultralytics YOLO tackles defect detection
One model, every defect type
Ultralytics YOLO detects, localizes, and classifies defects in a single pass, and researchers have pushed it past 98% mAP on benchmarks from PCBs to steel to wafers. That means fewer escaped defects, less scrap, and faster time-to-market, on the hardware you already run.
- Subtle defects, caught: 98.8% mAP on tiny PCB defects, 98.2% on wafer surfaces (peer-reviewed).
- Line-speed inference: YOLO26 runs a frame in ~1.7 ms; weld studies report 2.3 ms per part.
- Trainable on your defects: fine-tune on your own images in hours; published models hit 99%+ accuracy.
- Deploy anywhere: export to 19 formats — TensorRT, ONNX, OpenVINO, CoreML — for edge or cloud.
Try YOLO26 Inference
Drag and drop an image to see real-time object detection
Deploy on the line or in the cloud
Train once, then export to 19 formats and run wherever inspection happens, from a camera-side edge box to a GPU server.
At the edge (line-side)
- NVIDIA Jetson via TensorRT: real-time inference with no internet connection.
- Intel CPUs via OpenVINO: deploy on the industrial PCs you already run.
- Hailo, Raspberry Pi & ARM: low-power, camera-side inspection stations.
- Fast enough for the line: published weld inspection runs at 2.3 ms per part.
In the cloud
- GPU autoscaling for high-volume, multi-camera inspection.
- Retrain centrally and push new defect classes to every line at once.
- Export to ONNX, TensorRT & PyTorch for any inference stack.
Transforming industries with vision AI
From factory floors to operating rooms, Ultralytics turns visual data into real-time decisions.
SOHGA cuts parking monitoring time by 30% with Ultralytics YOLO
Learn how SOHGA's MEGURU system uses Ultralytics YOLO26 to automate parking lot patrols, reduce patrol time by 30%, and improve safety.
Scaleout cuts model updates from weeks to hours with Ultralytics YOLO
Discover how Scaleout uses Ultralytics YOLO and federated learning to fine-tune AI models on edge devices while keeping sensitive data secure.
RapiD Engineering deploys seafood quality control 1 week faster with Ultralytics YOLO
See how RapiD Engineering uses Ultralytics YOLO to automate salmon inspection, detect defects in real time, and save a week of engineering work.
Project Ocean Oasis advances reef conservation with Ultralytics YOLO
Discover how Project Ocean Oasis uses Ultralytics YOLO, edge AI, and autonomous monitoring systems to scale reef conservation and ocean intelligence.
Volley powers 250+ on-court AI trainers with Ultralytics YOLO
"What's really nice is that the model performs really well in real time on edge hardware on the trainer, and we can use the same model in the cloud to run the exact same flow."
WG Tech Solutions cuts safety violations by 28% with Ultralytics YOLO and Axelera’s AI Accelerator
Learn how WG Tech Solutions managed to reduce safety violations in manufacturing by 28% leveraging Ultralytics YOLO and Axelera’s AI Accelerator
Stride delivers 1-minute equine gait analysis with Ultralytics YOLO
Learn how Stride leverages Ultralytics YOLO models for horse pose estimation, achieving a full gait analysis in under 1 minute.
Pixelabs achieves 95% recall with Ultralytics YOLO-driven automation
Learn how Pixelabs leverages Ultralytics YOLO models to automate workflows and achieve 95% recall.
SiteAssist improves site safety by processing 770K+ images with Ultralytics YOLO
Learn how SiteAssist leverages Ultralytics YOLO models to improve construction site safety.
Chef Robotics uses Ultralytics YOLO to cut food giveaway by 67%
Explore how Chef Robotics uses Ultralytics YOLO models for precise food assembly.
Cali Intelligence shortens checkout queues by 43% with Ultralytics YOLO
Explore how Cali Intelligence uses Ultralytics YOLO models to reduce retail checkout queues with object detection.
MarineSitu hits 96%+ uptime in underwater monitoring using Ultralytics YOLO
Discover how MarineSitu uses Ultralytics YOLO to transform underwater object detection.
Theia Scientific speeds up microscopy analysis 43× with Ultralytics YOLO
Discover how Theia Scientific uses Ultralytics YOLO to redefine microscopy data analysis.
eSmart Systems halves power line inspection time with Ultralytics YOLO
Discover how eSmart Systems uses Ultralytics YOLO to boost defect detection speed and transform utility inspections.
Axelera AI delivers 34 FPS edge AI inference using Ultralytics YOLO
Discover how Axelera AI uses Ultralytics YOLO to deliver fast, accurate, and efficient edge vision on Metis AI chips.
STMicroelectronics runs Ultralytics YOLO on an MCU at just 9.4 mJ per inference
Discover how STMicroelectronics efficiently deploys Ultralytics YOLO models on low-power microcontrollers to achieve accurate real-time inference at the edge.
Specialvideo reaches 99% food inspection accuracy with Ultralytics YOLO
Discover how Specialvideo uses Ultralytics YOLO models to power real-time AI food inspection, ensuring quality, reducing waste, and boosting efficiency.
Vivity AI saves $5M+ a year in industrial operations with Ultralytics YOLO
Discover how Vivity AI leverages Ultralytics YOLO models to enhance industrial automation, improving efficiency, reducing downtime, and ensuring workplace safety.
Videologic Analytics scales to 10K AI camera licenses with Ultralytics YOLO
Discover how Videologic Analytics integrates Ultralytics YOLO models to enhance video surveillance, improving detection accuracy, reducing false alarms, and optimizing real-time threat monitoring.
Prezent boosts slide detection accuracy by 34% with Ultralytics YOLO
Discover how Prezent leverages Ultralytics YOLO models to automate slide element detection, reducing processing time to under 10 seconds while preserving structure and design.
ALYCE accelerates traffic AI inference by 20% with Ultralytics YOLO
Discover how ALYCE uses Ultralytics YOLO models to enhance data accuracy, optimize urban mobility, and create AI-driven traffic solutions for sustainable, smarter cities.
Kiwitron uses Ultralytics YOLO to detect industrial hazards 30m away
Explore how Kiwitron uses Ultralytics YOLO models in its KewiEye solution to detect and address industrial hazards for better safety.
SOHGA cuts parking monitoring time by 30% with Ultralytics YOLO
Learn how SOHGA's MEGURU system uses Ultralytics YOLO26 to automate parking lot patrols, reduce patrol time by 30%, and improve safety.
Scaleout cuts model updates from weeks to hours with Ultralytics YOLO
Discover how Scaleout uses Ultralytics YOLO and federated learning to fine-tune AI models on edge devices while keeping sensitive data secure.
RapiD Engineering deploys seafood quality control 1 week faster with Ultralytics YOLO
See how RapiD Engineering uses Ultralytics YOLO to automate salmon inspection, detect defects in real time, and save a week of engineering work.
Project Ocean Oasis advances reef conservation with Ultralytics YOLO
Discover how Project Ocean Oasis uses Ultralytics YOLO, edge AI, and autonomous monitoring systems to scale reef conservation and ocean intelligence.
Volley powers 250+ on-court AI trainers with Ultralytics YOLO
"What's really nice is that the model performs really well in real time on edge hardware on the trainer, and we can use the same model in the cloud to run the exact same flow."
WG Tech Solutions cuts safety violations by 28% with Ultralytics YOLO and Axelera’s AI Accelerator
Learn how WG Tech Solutions managed to reduce safety violations in manufacturing by 28% leveraging Ultralytics YOLO and Axelera’s AI Accelerator
Stride delivers 1-minute equine gait analysis with Ultralytics YOLO
Learn how Stride leverages Ultralytics YOLO models for horse pose estimation, achieving a full gait analysis in under 1 minute.
Pixelabs achieves 95% recall with Ultralytics YOLO-driven automation
Learn how Pixelabs leverages Ultralytics YOLO models to automate workflows and achieve 95% recall.
SiteAssist improves site safety by processing 770K+ images with Ultralytics YOLO
Learn how SiteAssist leverages Ultralytics YOLO models to improve construction site safety.
Chef Robotics uses Ultralytics YOLO to cut food giveaway by 67%
Explore how Chef Robotics uses Ultralytics YOLO models for precise food assembly.
Cali Intelligence shortens checkout queues by 43% with Ultralytics YOLO
Explore how Cali Intelligence uses Ultralytics YOLO models to reduce retail checkout queues with object detection.
MarineSitu hits 96%+ uptime in underwater monitoring using Ultralytics YOLO
Discover how MarineSitu uses Ultralytics YOLO to transform underwater object detection.
Theia Scientific speeds up microscopy analysis 43× with Ultralytics YOLO
Discover how Theia Scientific uses Ultralytics YOLO to redefine microscopy data analysis.
eSmart Systems halves power line inspection time with Ultralytics YOLO
Discover how eSmart Systems uses Ultralytics YOLO to boost defect detection speed and transform utility inspections.
Axelera AI delivers 34 FPS edge AI inference using Ultralytics YOLO
Discover how Axelera AI uses Ultralytics YOLO to deliver fast, accurate, and efficient edge vision on Metis AI chips.
STMicroelectronics runs Ultralytics YOLO on an MCU at just 9.4 mJ per inference
Discover how STMicroelectronics efficiently deploys Ultralytics YOLO models on low-power microcontrollers to achieve accurate real-time inference at the edge.
Specialvideo reaches 99% food inspection accuracy with Ultralytics YOLO
Discover how Specialvideo uses Ultralytics YOLO models to power real-time AI food inspection, ensuring quality, reducing waste, and boosting efficiency.
Vivity AI saves $5M+ a year in industrial operations with Ultralytics YOLO
Discover how Vivity AI leverages Ultralytics YOLO models to enhance industrial automation, improving efficiency, reducing downtime, and ensuring workplace safety.
Videologic Analytics scales to 10K AI camera licenses with Ultralytics YOLO
Discover how Videologic Analytics integrates Ultralytics YOLO models to enhance video surveillance, improving detection accuracy, reducing false alarms, and optimizing real-time threat monitoring.
Prezent boosts slide detection accuracy by 34% with Ultralytics YOLO
Discover how Prezent leverages Ultralytics YOLO models to automate slide element detection, reducing processing time to under 10 seconds while preserving structure and design.
ALYCE accelerates traffic AI inference by 20% with Ultralytics YOLO
Discover how ALYCE uses Ultralytics YOLO models to enhance data accuracy, optimize urban mobility, and create AI-driven traffic solutions for sustainable, smarter cities.
Kiwitron uses Ultralytics YOLO to detect industrial hazards 30m away
Explore how Kiwitron uses Ultralytics YOLO models in its KewiEye solution to detect and address industrial hazards for better safety.
SOHGA cuts parking monitoring time by 30% with Ultralytics YOLO
Learn how SOHGA's MEGURU system uses Ultralytics YOLO26 to automate parking lot patrols, reduce patrol time by 30%, and improve safety.
Scaleout cuts model updates from weeks to hours with Ultralytics YOLO
Discover how Scaleout uses Ultralytics YOLO and federated learning to fine-tune AI models on edge devices while keeping sensitive data secure.
RapiD Engineering deploys seafood quality control 1 week faster with Ultralytics YOLO
See how RapiD Engineering uses Ultralytics YOLO to automate salmon inspection, detect defects in real time, and save a week of engineering work.
Project Ocean Oasis advances reef conservation with Ultralytics YOLO
Discover how Project Ocean Oasis uses Ultralytics YOLO, edge AI, and autonomous monitoring systems to scale reef conservation and ocean intelligence.
Volley powers 250+ on-court AI trainers with Ultralytics YOLO
"What's really nice is that the model performs really well in real time on edge hardware on the trainer, and we can use the same model in the cloud to run the exact same flow."
WG Tech Solutions cuts safety violations by 28% with Ultralytics YOLO and Axelera’s AI Accelerator
Learn how WG Tech Solutions managed to reduce safety violations in manufacturing by 28% leveraging Ultralytics YOLO and Axelera’s AI Accelerator
Stride delivers 1-minute equine gait analysis with Ultralytics YOLO
Learn how Stride leverages Ultralytics YOLO models for horse pose estimation, achieving a full gait analysis in under 1 minute.
Pixelabs achieves 95% recall with Ultralytics YOLO-driven automation
Learn how Pixelabs leverages Ultralytics YOLO models to automate workflows and achieve 95% recall.
SiteAssist improves site safety by processing 770K+ images with Ultralytics YOLO
Learn how SiteAssist leverages Ultralytics YOLO models to improve construction site safety.
Chef Robotics uses Ultralytics YOLO to cut food giveaway by 67%
Explore how Chef Robotics uses Ultralytics YOLO models for precise food assembly.
Cali Intelligence shortens checkout queues by 43% with Ultralytics YOLO
Explore how Cali Intelligence uses Ultralytics YOLO models to reduce retail checkout queues with object detection.
MarineSitu hits 96%+ uptime in underwater monitoring using Ultralytics YOLO
Discover how MarineSitu uses Ultralytics YOLO to transform underwater object detection.
Theia Scientific speeds up microscopy analysis 43× with Ultralytics YOLO
Discover how Theia Scientific uses Ultralytics YOLO to redefine microscopy data analysis.
eSmart Systems halves power line inspection time with Ultralytics YOLO
Discover how eSmart Systems uses Ultralytics YOLO to boost defect detection speed and transform utility inspections.
Axelera AI delivers 34 FPS edge AI inference using Ultralytics YOLO
Discover how Axelera AI uses Ultralytics YOLO to deliver fast, accurate, and efficient edge vision on Metis AI chips.
STMicroelectronics runs Ultralytics YOLO on an MCU at just 9.4 mJ per inference
Discover how STMicroelectronics efficiently deploys Ultralytics YOLO models on low-power microcontrollers to achieve accurate real-time inference at the edge.
Specialvideo reaches 99% food inspection accuracy with Ultralytics YOLO
Discover how Specialvideo uses Ultralytics YOLO models to power real-time AI food inspection, ensuring quality, reducing waste, and boosting efficiency.
Vivity AI saves $5M+ a year in industrial operations with Ultralytics YOLO
Discover how Vivity AI leverages Ultralytics YOLO models to enhance industrial automation, improving efficiency, reducing downtime, and ensuring workplace safety.
Videologic Analytics scales to 10K AI camera licenses with Ultralytics YOLO
Discover how Videologic Analytics integrates Ultralytics YOLO models to enhance video surveillance, improving detection accuracy, reducing false alarms, and optimizing real-time threat monitoring.
Prezent boosts slide detection accuracy by 34% with Ultralytics YOLO
Discover how Prezent leverages Ultralytics YOLO models to automate slide element detection, reducing processing time to under 10 seconds while preserving structure and design.
ALYCE accelerates traffic AI inference by 20% with Ultralytics YOLO
Discover how ALYCE uses Ultralytics YOLO models to enhance data accuracy, optimize urban mobility, and create AI-driven traffic solutions for sustainable, smarter cities.
Kiwitron uses Ultralytics YOLO to detect industrial hazards 30m away
Explore how Kiwitron uses Ultralytics YOLO models in its KewiEye solution to detect and address industrial hazards for better safety.
Frequently asked questions
Automated defect detection uses cameras and AI to inspect products and surfaces for flaws, scratches, cracks, dents, missing parts, and contamination, in real time, with no manual checking. Ultralytics YOLO models detect, localize, and classify each defect in a single pass as parts move down the line.
It depends on the data, but recent peer-reviewed studies report 84-99% accuracy with YOLO models tuned to a specific task, for example 99.4% on fabric, 98.8% mAP on PCBs, and 98.2% on semiconductor wafers. The Ultralytics Platform supports active learning, so accuracy keeps climbing as new defect types appear.
Published 2024-2026 studies apply Ultralytics YOLO to steel and metal surfaces, printed circuit boards, semiconductor wafers, welds, textiles, solar panels, aluminum castings, ceramics, batteries, and pharmaceutical packaging, anywhere visual quality control matters.
Yes. Rule-based machine vision struggles with variable, subtle, or previously-unseen defects. Ultralytics YOLO26 learns from your own examples to recognize scratches, hairline cracks, white spots, and missing components across changing lighting and part variation, reaching 98%+ mAP on several published benchmarks.
Fast enough for the line. YOLO26 processes a frame in about 1.7 ms on a modern GPU; published inspection systems report 186 frames per second on fabric and 2.3 ms per weld.
Yes. Most inspection deployments run on-premises because production data is sensitive and factory networks are unreliable. Ultralytics YOLO26 exports to 19 formats, including TensorRT for NVIDIA Jetson, OpenVINO for Intel CPUs, and Hailo accelerators, so models run line-side with no internet connection required.
Less than you might expect. Published defect-detection models train on a few thousand labeled images, for example the bridge-crack study used a 4,029-image dataset. The Ultralytics Platform handles annotation and training, and pretrained YOLO weights mean you fine-tune rather than start from scratch.
Collect images of your good and defective parts, annotate them, and train, all in one place on the Ultralytics Platform. For production deployment, enterprise licensing covers commercial use and keeps your code and data private.
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