BASELABS’ Post

Excited to share my latest project where I implemented real-time traffic monitoring and management system using YOLOv8 large model! 🔧 Key Features: ⭕ Custom YOLOv8 Large Model: Trained on a custom dataset specifically designed to recognize vehicles in various traffic scenarios, improving detection accuracy and robustness. ⭕ Real-Time Vehicle Tracking: Each detected vehicle is continuously tracked across frames, allowing for real-time lane occupancy monitoring and accurate counting of vehicles entering/exiting lanes. ⭕ Ring Area Detection: Implemented a ring-area detection logic to monitor vehicles within a specific radius, allowing for additional analysis such as zone-specific traffic flow or restricted areas. ⭕ Efficient Video Processing: The system is optimized to process high-resolution video streams, ensuring minimal latency and smooth operation even with large video datasets. ⭕ Polygonal Lane Detection: Each lane is defined by custom polygons, ensuring that vehicles are tracked within specific zones with high precision. This project demonstrates the practical use of computer vision and deep learning in real-time traffic monitoring and management systems. By leveraging the power of YOLOv8 Large, I’ve been able to create an efficient and scalable solution that could be deployed in smart cities for traffic flow analysis, security, and management. Dataset - https://lnkd.in/g5_s845t *️⃣ *️⃣ *️⃣ As I continue to improve my skills in AI and machine learning, I would appreciate advice from the LinkedIn community on how to further enhance my potential!!!! #YOLOv8 #VehicleDetection #AI #MachineLearning #ComputerVision #DeepLearning #TrafficManagement #RealTimeSystems #SmartCities #AIApplications #TechInnovation #ComputerVisionApplications

Hallucinations are a perennial problem with AI. Detecting them is tricky but essential especially when the failure rate is so high. 1.3% is the best performing system and is miles from our expectation of “five 9’s” reliability ie 99.999% accurate. That’s what we expect from aircraft and industrial equipment. That’s a 1000 times worse. It’s a long road ahead.

Unveiling AI use cases at operational scale 🤖 At Juniper Networks’ Seize the AI Moment event, Alexander Heine from Deutsche Bahn discusses how AI elevates the company’s transportation operations and processes. “We use AI for capacity traffic management and recalculating the best way a train can go with restrictions like weather incidents or different kinds of track requirements. Not every train can go to the special tracks, and this has a very big data amount needed to make an AI prediction. For this use case, we use reinforcement learning to find new ways to use the capacity on the track much better,” shares Heine. “A second use case is that we want to go from now we're developing ROA2 systems, Rate of Automation 2,” he furthers. “It means that we have train drivers that supervise the train. The train brakes and drives on its own, and the train driver must take over the control every time.” “Our main vision is to have here a fully automated driving train that detects the environment by themselves and reacts in the right way. So this is a classical, the ROA4 approach that we try to implement here to use automated train operating trains in shunting areas,” he concludes. 📺 Watch the complete session: https://lnkd.in/g7mscxp4 #theCUBE #AINativeNow #AIinfra

𝐋𝐨𝐠𝐢𝐜 𝐅𝐫𝐮𝐢𝐭’𝐬 𝐀𝐈/𝐌𝐋 𝐃𝐞𝐦𝐨 𝐕𝐢𝐝𝐞𝐨 𝐨𝐟 𝐂𝐨𝐥𝐥𝐢𝐬𝐢𝐨𝐧 𝐀𝐯𝐨𝐢𝐝𝐚𝐧𝐜𝐞 (𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐑𝐚𝐧𝐠𝐢𝐧𝐠) 🚗💡 In this video, we showcase our advanced system leverages computer vision and machine learning to detect vehicles, calculate real-time distances, and issue alerts when vehicles are too close, ensuring timely intervention to avoid potential collisions. This solution significantly enhances road safety, reducing accident risks across diverse driving conditions. Watch now to see it in action! #AI #MachineLearning #CollisionAvoidance #RoadSafety #TechInnovation #LogicFruit

An AI Meter project landing page.

Ever hit a pothole so hard, you felt it in your soul? What if AI could spot .... Today in "AI for Dummies" we're tackling an everyday urban challenge—pothole detection. We've explored the depths of R-CNNs; now, let’s see how its faster sibling, Faster R-CNN, transforms this application to operate in real time. AI Meets the Street: Pothole Detection 😂 Potholes aren’t just annoying; they're a hazard. Thankfully, Faster R-CNN offers a quick and efficient solution by enabling real-time detection on the go. How Does Faster R-CNN Detect Potholes in Real Time? Cameras mounted on vehicles continuously capture road conditions while in motion. Unlike its predecessors, Faster R-CNN processes these images in real time, thanks to its streamlined architecture which includes a Region Proposal Network (RPN). This network quickly predicts areas within an image (like parts of the road) where objects (or potholes) might be located. The system instantly classifies detected potholes and their precise locations are mapped and reported to the relevant authorities or integrated into GPS systems for real-time alerts. The Impact of Real-Time AI Pothole Detection: 1. Helps cities address road issues before they worsen or cause accidents. 2. Minimizes the expenses linked to road damages and extensive manual surveys. 3. Provides drivers with real-time updates about road conditions, significantly enhancing road safety. By leveraging Faster R-CNN, cities can not only detect but also respond to road damage as soon as it's identified, preventing minor issues from becoming major inconveniences. This is a prime example of how cutting-edge AI technologies like Faster R-CNN are not just theoretical but highly applicable in our day-to-day lives. PS: Have you ever wished for a real-time fix to everyday problems like potholes? What if AI was on the case? What other day-to-day issues do you think AI should address next? #ai #daytoday #technology #cnn #objectdetection -- Stay tuned for more insights from my AI perspective by following me, Ivan. Keep up with the latest in AI by following Imagga. And remember, follow your passion, and you'll thrive!

Are you curious to know how engineers are using AI today? A top European racing team is now capturing and analysing vehicle dynamics data smarter and faster with our AI-powered tools. In the high-pressure world of racing, interpreting test data quickly is crucial. With Monolith's deep-learning anomaly detector tool, they can analyse hundreds of data channels in minutes, spotting over 90% of known errors. This automated inspection and validation process not only reduces manual effort but also ensures the car's peak performance on the track. Want to learn more? We have put together 8 case studies of how AI is helping test engineers today in this exclusive white paper: https://lnkd.in/ddfjDqEH #AI #racing #engineering #Monolith #DeepLearning #Automation #data #machinelearning

🚀 Excited to unveil my latest project: a cutting-edge #YOLOv8 Object Detection and #Tracking Application. Leveraging advanced AI and computer vision techniques, this tool detects and tracks objects in real-time with impressive accuracy and speed. 🖥️📸 Key Features: High-resolution video support with smooth playback Comprehensive object detection with detailed tracking information User-friendly interface with intuitive controls Efficient data saving and export options Proud of the innovative strides made and excited for the potential applications across various fields, from surveillance to automation. Ready to see what the future holds with AI-driven technology! 🌟 #AI #ComputerVision #YOLOv8 #ObjectDetection #TechInnovation

Mapping process to Optimize AI camera

It's always a pleasure when Government Technology shines a light on the work Rekor is doing in the public sector. Check out our latest case study on "How states are improving roadway incident responses with AI"! Mike Dunbar Tate Ewing Alex Devers Ryan Reynolds Paul-Matthew Zamsky #roadwayintelligence #rekor #govtech #ai #digitalinfrastructure https://lnkd.in/eB4tjGAf