objectdetection.app

#Object Detection | Applications

#Anyverse | Synthetic data software platform | Hyperspectral render emgine | Sensor simulation | Scene generation

#Solomon | SolVision | AI Visual Inspection | Deep Learning Software

#Viso | Viso Suite | No code application development platform | Computer vision

#SEA.AI App | Detecting floating objects early | Using thermal and optical cameras to catch unsignalled craft, floating obstacles, containers, buoys, inflatables, kayaks and persons over board

#NVIDIA | MLPerf | Measuring time to train neural networks | Natural language processing | Speech recognition | Recommender systems | Biomedical image segmentatione | Object detection | Image classification | Reinforcement learning | NVIDIA benchmarking suite | MLPerf Training | MLPerf Inference | MLPerf HPC | NVIDIA Platform

#IEEEE | Recurrent Vision Transformers for Object Detection with Event Cameras

#Aalto University | Intelligent Robotics | Robotic Perception | Robot to robot interaction | Robotic grasping, knowing the object shape

#Advanced Navigation | AI-based marine navigation systems | AI-Based underwater navigation solutions and robotics technology | Hydrography | Underwater acoustic positioning solutions | Autonomous Underwater Vehicle (AUV) | Inertial navigation systems (INS) | Sidney, Australia

#Glidance | Walking companion | Passive kinetic guidance through pole | Handle connected to small mobile platform | Wheels that steer and break | Identifies user location | Detects both static and dynamic obstacles | Steers user | Applyis and releases brakes

#Sanctuary AI | Humanoid robot | Touch and grasping | Coordination between touch sensors and vision systems | Visual servoing | Real-time simulation of the grasping process | Mapping between visual and haptic data

#OndoSense | Radar distance sensor | Sensor software: integrated into control system or used for independent quality monitoring | Object detection | Distance measurement | Position control | Agriculture: reliable height control of the field sprayer | Mining industry | Transport & Logistics | Shipping & Offshore | Mechanical and plant engineering | Metal and steel industry | Energy sector | Harsh industrial environments | Dust & smoke: no influence | Rain & snow: no influence | Radar frequency: 122GHz | Opening angle: ±3° | Measuring range: 0.3 – 40 m | Measuring rate: up to 100Hz | Output rate: up to 10 ms / 100 Hz | Measurement accuracy; up to ±1mm | Measurement precision: ±1mm | Communication protocol: RS485; Profinet, other interfaces via gateway | Switching output: 3x push-pull (PNP/NPN) | Analogue output: Current interface (4 – 20 mA) | Protection class: IP67

#Anybotics | Workforce App | Operate ANYmal robot from device | Set up and review robot missions | Industrial Inspection

#IDS | Industrial image processing | 3D cameras | Digital twins can distinguish color | Higher reproducible Z-accuracy | Stereo cameras: 240 mm, 455 mm | RGB sensor | Distinguishing colored objects | Improved pattern contrast on objects at long distances | Z accuracy: 0.1 mm at 1 m object distance | SDK | AI based image processing web service | AI based image analysis

#LookOut | AI vision system | Synthesized data from charts, AIS, computer vision, and cloud fusing it into one 3D augmented reality view | Connects to existing boat display | Mountable camera system to the top of any boat | Lookout App for laptop, phone or tablet | Infrared vision | Night vision sensor | Spotting small vessels, floating debris, buoys, people in water | Blind spot detection | Backup camera | Temperature breaks, bird cluster locations, underwater structures for anglers | Camera streaming over WiFi to phones and tablets on the boat | Over-the-air (OTA) updates | Marine-grade water-proof enclosure | Integrated with satellite compass | National Marine Electronics Association (NMEA) communication standard interface | Multifunction Display (MFD) | Multi-core CPU driving augmented reality compute stack | ClearCloud service | NVIDIA RTX GPU for real-time computer vision | DockWa app

#SiLC | Machine Vision solutions with FMCW LiDAR vision | FMCW at the 1550nm wavelength | Eyeonic Vision Sensor platform | Detecting vehicles and various obstacles from long distances | Honda Xcelerator Ventures | Honda Marine

#Flyability | Drones for industrial inspection and analysis | Confined space inspection | Collision and crash resistant inspection drone | 3D mapping | Volumetric measurement | Inspections of cargo ships, bridges, ports, steel mills cement factories, liquid gas tanks, nuclear facilities, city wide underground sewage systems | Ouster lidar

#National Technical University of Athens | MariNeXt deep-learning framework detecting and identifying marine pollution | Sentinel-2 imagery | Detecting marine debris and oil spills on sea surface | Automated data collection and analysis across large spatial and temporal scales | Deep learning framework | Data augmentation techniques | Multi-scale convolutional attention network | Marine Debris and Oil Spill (MADOS) dataset | cuDNN-accelerated PyTorch framework | NVIDIA RTX A5000 GPUs | NVIDIA Academic Hardware Grant Program | AI framework produced promising predictive maps | Shortcomings: unbalanced dataset, marine water and oil spills are abundant, foam and natural organic material are less represented

#Boston Dynamics | Ultrasonic inspections of rotating equipment with Fluke SV600 | Early detection of bearing failures before they lead to costly breakdowns | Operators can capture critical data without the need for additional inline sensors or manual inspections | Leica BLK ARC payload | Autonomous routines | Repeatable laser scans | Precise 3D data for tasks like factory design, equipment installation, and change management | Spot can identify and avoid obstacles requiring additional caution in dynamic environments, including carts, wires, and ladders | Spot Hardware Updates | Tablet Controller Pro | Ergonomic joysticks, longer battery life, and HDMI pass-through | Spot robit operates in temperatures up to 55°C | Core I/O antenna upgrade enhances range and reliability | AI Institute | Artificial intelligence (AI) | Robotics | Intelligent machines | Cognitive AI | Athletic AI | Organic hardware design

#Google DeepMind Technologies Limited | Creating advanced AI models and applications | Artificial intelligence systems ALOHA Unleashed and DemoStart | Helping robots perform complex tasks that require dexterous movement | Two-armed manipulation tasks | Simulations to improve real-world performance on multi-fingered robotic hand | Helping robots learn from human demonstrations | Translating images to action | High level of dexterity in bi-arm manipulation | Robot has two hands that can be teleoperated for training and data-collection | Allowing robots to learn how to perform new tasks with fewer demonstrations | Collectung demonstration data by remotely operating robot behavior | Applying diffusion method | Predicting robot actions from random noise | Helpung robot learn from data | Collaborating with DemoStart | DemoStart is helping new robots acquire dexterous behaviors in simulation | Google collaborating with Shadow Robot

#Movu Robotics | Robotics | Automated pallet storage solution | Accessible cold storage automation | Temperature-controlled food and pharmaceutical industry | Space optimisation in storage | Streamlined materials handling process | Energy efficiency for cold storage | Robotics for low temperatures with icy and slippery surfaces | Automated Storage and Retrieval System (AS/RS) | Automating picking | Fleet management software

#Neptune Labs | neptune.ai | Tracking foundation model training | Model training | Reproducing experiments | Rolling back to the last working stage of model | Transferring models across domains and teams | Monitoring parallel training jobs | Tracking jobs operating on different compute clusters | Rapidly identifying and resolving model training issues | Workflow set up to handle the most common model training scenarios | Tool to organize deep learning experiments

#Genetec | Security Center Platform | Ouster Gemini integrated | Fusing lidar and video surveillance into a single interface | Unifying customer data from lidar, cameras and radar | Physical intrusion detection in real-time | Using 3D data to power automated detection, classification, tracking, and monitoring | Measuring the distance, trajectory or speed of people or vehicles | Quickly identifying and responding to real threats with confidence | Reducing false alarms

#Linux Foundation | LF AI & Data | Fostering open source innovation in artificial intelligence and data | Open Platform for Enterprise AI (OPEA) | Creating flexible, scalable Generative AI systems | Promoting sustainable ecosystem for open source AI solutions | Simplifying the deployment of generative AI (GenAI) systems | Standardization of Retrieval-Augmented Generation (RAG) | Supporting Linux development and open-source software projects | Linux kernel | Linus Torvalds

Allen Institute for Artifical Intelligence | Robot planning precise action points to perform tasks accurately and reliably | Vision Language Model (VLM) controlling robot behavior | Introducing automatic synthetic data generation pipeline | Instruction-tuning VLM to robotic domains and needs | Predicting image keypoint affordances given language instructions | RGB image rendered from procedurally generated 3D scene | Computing spatial relations from camera perspective | Generating affordances by sampling points within object masks and object-surface intersections | Instruction-point pairs fine-tune language model | RoboPoint predicts 2D action points from image and instruction, which are projected into 3D using depth map | Robot navigates to these 3D targets with motion planner | Combining object and space reference data with VQA and object detection data | Leveraging spatial reasoning, object detection, and affordance prediction from diverse sources | Enabling to generalize combinatorially.| Synthetic dataset used to teach RoboPoint relational object reference and free space reference | Red and ground boxes as visual prompts to indicate reference objects | Cyan dots as visualized ground truth | NVIDIA | | Universidad Catolica San Pablo | University of Washington

#Intergovernmental Negotiating Committee (INC-5) | Developing international legally binding instrument on plastic pollution | Raising awareness about the serious impacts of plastic pollution on both humans and nature | Global bans and phase-outs of the most harmful and problematic plastic products and chemicals | Global product design requirements to ensure all plastic produced is safe to reuse and recycle as part of global non-toxic circular economy

#Tampere University | Pneumatic touchpad | Soft touchpad sensing force, area and location of contact without electricity | Device utilises pneumatic channels | Can be used in environments such as MRI machines | Soft robots | Rehabilitation aids | Touchpad does not need electricity | It uses pneumatic channels embedded in the device for detection | Made entirely of soft silicone | 32 channels that adapt to touch | Precise enough to recognise handwritten letters | Recognizes multiple simultaneous touches | Ideal for use in devices such as MRI machines | If cancer tumours are found during MRI scan, pneumatic robot can take biopsy while patient is being scanned | Pneumatic device can be used in strong radiation or conditions where even small spark of electricity would cause serious hazard

#Cyclopharm | Nuclear medicine device | Patient breathing superheated radioactive, gas-like substance | Better lung imaging.| Detected pulmonary embolisms (blood clots) | Visualization of pulmonary ventilation | Chronic obstructive pulmonary disease (COPD) | Asthma | Long Covid.| Dry-carbon nano-particles irradiated with isotope technetium-99 | Particles are 150 nano-metres | Heating carbon crucible to 2,700 degrees Celsius | Only three to four breaths required | Gas works as imaging agent | Computed tomography (CT) camera | Nanoparticles have six-hour radioactive life | Technegas images fine alveoli, where oxygen and blood mix | Technegas suitable to pregnant, to those having poor kidney function or who are allergic to imaging agents | Device is fully reimbursed, stand-alone | Technegas generator

#JCB | Artificial intelligence designed to drastically reduce the risk of harm and injury on construction sites | Four cameras | Three AI cameras (two side mounted and one rear-facing) | Forward-facing standard camera | Object detection | Factory-fit solution on JCB loadall telescopic handlers

#NVIDIA | GH200 Grace Hopper Superchip | Boosting TTFT in multiturn user interactions | Building contextual understanding of input sequence | Retrieval Augmented Generation (RAG) | Converting user prompt to tokens and then to highly dense vectors | Dot product operations | Building mathematical representation of the relationship between all tokens in prompt | Operations repeated across different layers of the model | Generating key-value cache (KV cache) | Reusing KV cache | KV offloaded from GPU memory to higher capacity and lower cost CPU memory | KV cache reloading back to GPU memory | KV cache offloading, eliminates need to recompute KV cache without holding up valuable GPU memory | Enabling multiple users to interact with the same content without recalculating KV cache for each new user | Speeding code generation in integrated development environments (IDEs) that have LLM capabilities, one or more developers can submit multiple prompts interacting with single code script over extended periods of time, offloading initial KV cache calculations onto CPU memory and then reloading it for subsequent interactions avoids repeated recalculations and saves valuable GPU and infrastructure resources | NVIDIA TensorRT-LLM | Llama 3 70B model running on server with NVIDIA H100 Tensor Core GPUs connected through PCIe to x86 host processor | Compelling deployment strategy for data centers

#Australian Meteorological & Oceanographic Society | Oceanography

#Ouster | BlueCity | Powering Lidar-Enabled Smart Traffic Solution | Traffic management solution in Chattanooga, Tennessee | Improvung roadway safety | Reducing congestion.| BlueCity solution to over 120 intersections | Combining digital lidar sensors and edge AI at each intersection | Managing traffic flow | Detecting and analyzing safety incidents | Providing detection for vehicle-to-everything (V2X) communications | Advanced perception software from Ouster | Lidar-powered smart traffic network | Optimizing traffic signal management on roads and intersections | Providing data to improve pedestrian safety | Intelligent signal actuation at intersections | Generating analytics data stream to traffic operators | Creating real-time 3D digital traffic twin of an intersection or road | Automating data collection in the cloud | Monitoring road events more accurately for vehicles, pedestrians and cyclists | Quick safety interventions | Long-term planning optimizations | Deep learning AI perception | Object classification | Object detection | Traffic actuation | Near-miss detection | Outside of crosswalk events | Red light running | Wrong-way driving | Southern Lighting & Traffic Systems | Center for Urban Informatics & Progress (CUIP) | University of Tennessee Chattanooga Research Institute (UTCRI) | Certified lidar traffic solution with Buy America(n) lidar

#Robotics & AI Institute | Collaborates with Boston Dynamics | Developed jointly Reinforcement Learning Researcher Kit for Spot quadruped robot | Developing sim-to-real for mobility | Transferring simulation results to real robotic hardware | Bridging sim-to-reality gap | Training policies generating a variety of agile behavior on physical hardware | Trying to achieve novel, robust, and practical locomotion behavior | Improving whole body loco-manipulation | Developing robot capability to manipulate objects and fixtures, such as doors and levers, in conjunction with locomotion significantly enhancing its utility | Exploring new policies to improve robustness in scenarios | Exploring full-body contact strategies | Exploring high-performance, whole-body locomotion and tasks that require full-body contact strategies, such as dynamic running and full-body manipulation of heavy objects, necessitating close coordination between arms and legs | Aiming to utilize reinforcement learning to generate behavior during complex contact events without imposing strict requirements | Develop technology that enables future generations of intelligent machines | Streamlining processes for robots to achieve new skills | Developing perception, situational understanding, reasoning, cognitive functions underpinning robot abilities and combining them with advances in their physical capabilities | Conducting research in four core areas: cognitive AI, athletic AI, organic hardware design, and ethics related to robotics

#UC Berkeley, CA, USA | Professor Trevor Darrell | Advancing machine intelligence | Methods for training vision models | Enabling robots to determine appropriate actions in novel situations | Approaches to make VLMs smaller and more efficient while retaining accuracy | How LLMs can be used as visual reasoning coordinators, overseeing the use of multiple task-specific models | Utilizing visual intelligence at home while preserving privacy | Focused on advancements in object detection, semantic segmentation and feature extraction techniques | Researched advanced unsupervised learning techniques and adaptive models | Researched cross-modal methods that integrate various data types | Advised SafelyYou, Nexar, SuperAnnotate. Pinterest, Tyzx, IQ Engines, Koozoo, BotSquare/Flutter, MetaMind, Trendage, Center Stage, KiwiBot, WaveOne, DeepScale, Grabango | Co-founder and President of Prompt AI

#Thinking Machines Lab | thinkingmachines.ai | Building artificial intelligence models and products | Competing on high end of large language models | Human-AI collaboration | Building AI that can adapt to full spectrum of human expertise | Multimodal systems that work with people collaboratively | AI models that can work across text, audio, video | AI models designed to excel in science and programming | Publishing technical blog posts, papers, program code | Mira Murati: CEO | John Schulman: Chief Scientist | Barret Zoph: CTO | Alexander Kirillov: Multimodal Research Head | John Lachman: Head of Special Projects | Alex Gartrell: Linux kernel, networking, and containerization | Andrew Tulloch: ML systems research and engineering | Brydon Eastman: Human and synthetic data, model alignment and RL | Christian Gibson: Supercomputers used in training frontier models | Devendra Chaplot: VLMs, RL, & Robotics | Ian O Connell: Infrastructure engineering | Jacob Menick: ML researcher | Joshua Gross: Products and research | Kurt Shuster: Reasoning | Kyle Luther: ML researcher | Lilian Weng: Research | Luke Metz: Research scientist and engineer | Mario Saltarelli: IT and Security leader | Myle Ott: AI researcher | Nikki Sommer: HRBP | Noah Shpak: ML Engineer, GPUs | Pia Santos: Executive Operations Leader | Randall Lin: Algorithms | Rowan Zellers: Realtime multimodal posttraining | Sam Schoenholz: Scaling, optimization | Sam Shleifer: Inference | Stephen Chen: Infrastructure engineer | Stephen Roller: Full-stack pre-training | Yinghai Lu: ML system engineer

#Caterpillar | Cat 775 | Off-highway truck | MineStar Command for hauling | 360-degree surround cameras with object detection | Cat Detect radar system | Frame design reducing empty weight delivering | Redesigned suspension for lower centre of gravity | Electronic powertrain controls | Automatically detecting hazards within critical areas around truck | Ability to access and analyse accurate real-time data from truck | Access to the latest software updates | Truck software updates scheduled and executed at a time that does not interrupt the production schedule | Remote Troubleshoot enabling dealer to perform diagnostics remotely while truck is still in operation

#MemryX | AI Accelerator Module | Install system software, MemryX SDK, MemryX board | Compile AI model(s) making executable file | Send data & receive results using APIs for AI processing | Up to 6 TFLOPs (1GHz) per chip | Up to 16-chips (96 TOPS/TFLOPs) can be interconnected | Activations: bfloat16 (high accuracy) | Weights: 4 / 8 / 16 bit | Batch = 1 | 10.5M 8-bit parameters (weights) per chip | PCIe Gen 3 I/O | USB 3 interface | 0.6-2W per chip av power | Smart compiler: optimized and automated AI mapping to MemryX hardware | Powerful APIs: Python and C/C++ low and mid-level APIs for AI integration | Runtime: driver and firmware to support Windows or Linux distributions | Bit Accurate simulator: accurately testing models even without MemryX hardware