Приклади вживання Object detection Англійська мовою та їх переклад на Українською
{-}
-
Colloquial
-
Ecclesiastic
-
Computer
Object Detection.
Fine-tuned atomic clocks and object detection.
Object detection and image segmentation.
Central, face recognition, AF with object detection.
The accuracy of object detection is also improved from 14 to 81 percent.
Then we will build a working object detection system.
Air object detection probability 0.9 at signal-to-noise ratio +3 dB and false alarm probability 0.0001;
They also handle FOD(Foreign Object Detection) differently.
The extended list of security systemsincludes a moving object recognition system(Moving Object Detection).
The technology boosted the object detection accuracy from 14% to 81%.
Exterior cameras help cut down on blind spots and aid with object detection.
It is also said to improve object detection accuracy from 14% to 81%.
Additionally, neural networks are used for image processing and object detection.
Knowledge of performance metrics in object detection and classification, such as mAP and related.
You will be able to independently build a system for object detection tasks.
Surround cameras aid object detection and minimise blind spots, automatically alerting the driver to safety hazards and obstacles.
Next-generation radar technology can use advanced algorithms for object detection….
The technology of object detection in outdoor advertising used by the Company Ocean Outdoor(UK) in partnership with Renault.
Compared to some of the cameras found on vehicles at the moment,Mitsubishi's tech extends the maximum distance of object detection from 30 to 100 meters.
Practical experience in at least one of the following problems: object detection, segmentation, face recognition, person re-id, action recognition, generative models;
Two neural networks were used to localize a carpet on the photo:the Region Proposal Network to generate candidate regions and the Object Detection Network.
Oles has extensive experiencein video processing using Deep learning methods for object detection and action, image caption generation, and Hollywood movie studio videos.
I work on a problem called object detection, where we look at an image and try to find all of the objects, put bounding boxes around them and say what those objects are.
Cameras that see polarised light are currently used to detect material stress,enhance contrast for object detection, and analyze surface quality for dents or scratches.
In the past, object detection systems would take an image like this and split it into a bunch of regions and then run a classifier on each of these regions, and high scores for that classifier would be considered detections in the image.
There are many well-known models that are used for different tasks, for example Inception is a widely used model forimage recognition, YOLO is used for object detection, FaceNet for facial recognition, TextCNN for textual sentiment analysis, and so on.
In the paper"Sharing visual features for multiclass and multiview object detection", A. Torralba et al. used GentleBoost for boosting and showed that when training data is limited, learning via sharing features does a much better job than no sharing, given same boosting rounds.
Tracking objects in Sports requires specific complex solutions: how to combine multiple NNs in onemodular system to solve different tasks like object detection, classification, clusterization.