Ultralytics

Construction disputes can take weeks to resolve, and every delay impacts a project's ROI. OnsiteIQ tackles this by capturing 100% of every job site with 360° cameras, then running that imagery through an AI pipeline powered by Ultralytics YOLO11. The result: a continuous, visual record that turns "what's responsible for this delay?" into a question that takes minutes to answer, not weeks. The impact across 3,000+ projects and $34B+ in development: ✅ 3x faster dispute resolution ✅ 20% reduction in project delays ✅ 24% increase in delivery predictability ✅ $25K–$400K in cost overruns avoided per project Read the full case study ➡️ https://bit.ly/4qbuakw

What if a crosswalk could recognize danger before an accident happens? 🚸 Most intersections only react after an incident is reported. But with computer vision, cities can continuously observe traffic patterns and identify risky behaviors as they emerge. Using Ultralytics YOLO26, camera systems can monitor pedestrian crossings, analyze vehicle movement, and surface situations where safety may be compromised, such as drivers failing to yield or recurring near-miss events. The value extends beyond enforcement: 󠁯• Understand how pedestrians actually use intersections 󠁯•󠁏 Identify locations with recurring risky interactions 󠁯• Measure the impact of infrastructure improvements 󠁯 As cities embrace intelligent transportation systems, computer vision is helping transform traffic cameras from passive recording devices into tools for safer, more responsive streets. Learn more ➡️ https://lnkd.in/ed36sYug #Ultralytics #YOLO26 #SmartCities #RoadSafety

New tutorial | Export Ultralytics YOLO26 to Google LiteRT for Mobile AI 📱 Running computer vision directly on mobile devices can reduce latency, improve privacy, and enable applications to work without relying entirely on cloud infrastructure. In this tutorial, we explore LiteRT and demonstrate how to export YOLO26 for deployment across Android and edge environments. What'll be covered: ✅ Key features of Google LiteRT ✅ LiteRT performance on Xiaomi 17 ✅ Exporting YOLO26 to LiteRT with the Ultralytics Python package ✅ Exporting YOLO26 using the Ultralytics Platform A practical walkthrough for taking YOLO26 from model development to optimized mobile computer vision deployment. Watch now ➡️ https://lnkd.in/eyCuC49g #Ultralytics #YOLO26 #LiteRT #MobileAI #ComputerVision

The biggest bottleneck in many computer vision projects isn't the model; it's the data. 📊 Before teams can train, validate, and deploy vision AI solutions, they often spend significant time searching for relevant datasets, evaluating data quality, and building benchmarks. The Ultralytics Platform "Explore" page brings together community-contributed datasets across a wide range of industries and use cases, making it easier to discover training data, evaluate new ideas, and accelerate computer vision development. Whether you're building solutions for manufacturing, healthcare, retail, transportation, agriculture, or smart cities, access to diverse datasets can help reduce development time and improve experimentation workflows. The right dataset is often where that journey begins. Explore community datasets ➡️ https://lnkd.in/e2gPwR29 #Ultralytics #Platform #ComputerVision #Datasets

New release Ultralytics v8.4.118 | Standalone OpenAI-compatible `LLM` interface 🤖 Use Ultralytics for YOLO vision and OpenAI-compatible language models, with multimodal requests plus more reliable training and dataset workflows. Minor updates: ✅ Improved OBB orientation through clipped augmentations ✅ Faster CopyPaste augmentation ✅ More stable training, inference, and classification workflows Ultralytics v8.4.118 release notes ➡️ https://lnkd.in/eKnjXmHU

Every empty shelf is a missed sales opportunity. 🛒 Retailers invest heavily in forecasting, inventory management, and merchandising, yet many decisions are still made using periodic store audits and manual shelf checks. By the time an issue is discovered, the revenue impact may have already occurred. Vision AI changes that. Using Ultralytics YOLO26 for object detection and segmentation, retailers can continuously analyze shelf conditions through camera feeds, transforming in-store activity into real-time operational intelligence. From a single deployment, teams can: 👉 Identify out-of-stock products before they affect sales 👉 Track share-of-shelf across brands and product categories 👉 Monitor inventory visibility without manual audits 👉 Surface merchandising opportunities as they happen The result isn't just better shelf monitoring; it's faster decision-making across store operations, inventory planning, and retail execution. Read more ➡️ https://lnkd.in/exws3ArF #Ultralytics #YOLO26 #Retail

How can computer vision streamline workflows and safety in infrastructure & transportation? mtrail GmbH built TrainVision to streamline manual processes like recording wagon numbers on live tracks. Powered by Ultralytics YOLO, it automatically detects European Vehicle Numbers, hazard signs, and brake signs in real time, right on handheld devices in the field. The results: ✅ Close to 100% reduction in data collection time ✅ Up to 90% fewer errors vs. manual entry ✅ 8–30ms processing per image, even on mobile ✅ Safer track conditions for rail personnel Read the full case study ➡️ https://bit.ly/4g1Bdro

Rockchip deployment just became more versatile 🚀 Ultralytics YOLO26 now supports RKNN export across all seven vision tasks: ✅ Object detection ✅ Instance segmentation ✅ Semantic segmentation ✅ Image classification ✅ Pose estimation ✅ Oriented object detection ✅ Depth estimation With one consistent workflow across a broader range of computer vision applications, edge developers can spend less time maintaining separate deployment pipelines and more time building efficient solutions for Rockchip hardware. Our goal remains simple: make the path from a trained model to an efficient edge application as direct, consistent, and repeatable as possible. Explore more ➡️ https://lnkd.in/eHgzKnD8 #Ultralytics #YOLO26 #EdgeAI #ComputerVision #RKNN

Edge AI adoption isn't limited by models anymore; it's often limited by deployment efficiency. ⚡ A community developer recently built an INT8-compatible RKNN export workflow for deploying Ultralytics YOLO26s on the Rockchip RK3588, achieving an impressive 40.9ms inference time on embedded hardware. This is a great example of how the Ultralytics ecosystem continues to expand beyond model development and into real-world deployment scenarios. As organizations look to run computer vision workloads closer to where data is generated, efficient inference on affordable edge devices becomes increasingly important. From smart cameras and industrial automation to robotics and embedded vision systems, optimized deployments on platforms like RK3588 can help reduce infrastructure requirements while maintaining real-time performance. Check out the benchmark and implementation details ➡️ https://lnkd.in/eFiEfWyU #Ultralytics #YOLO26 #EdgeAI #Rockchip

New release Ultralytics v8.4.117 | More reliable augmentation and safer deployment 🚀 This release strengthens annotation handling, improves cross-backend depth consistency, and accelerates YOLO26 TensorRT postprocessing without changing mAP. Minor updates: ✅ Safer dependency installation ✅ More robust dataset and mask processing ✅ Improved pose, OBB, tracking, and export support Ultralytics v8.4.117 release notes ➡️ https://lnkd.in/egK5ZfXb