How does objection detection work?
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Object detection involves various methods such as utilizing deep neural networks for feature extraction, region proposal generation, and classification. In the context of IoT networks, object detection can be optimized by partitioning neural network models and applying progressive transmission of convolutional filter maps to adapt to varying bandwidth constraints. For UAV applications, a combination of direct and feature methods, along with depth filters and convolutional neural networks like YOLOv3, enable fast indoor positioning and 3D target detection in real-time tasks. Additionally, foreign object detection in wireless energy transfer systems involves sampling analog signals, comparing power loss to expected values, and generating signals indicating the presence of foreign objects. Another approach involves using electromagnetic radiation detectors to identify objects passing through a field of view.
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| Papers (5) | Insight |
|---|---|
| Object detection in UAVs involves using CNNs like YOLOv3 to detect specific targets in images, associating them with spatial map points, and filtering out noise points for accurate localization. | |
Patent 28 Feb 2019 5 Citations | Foreign object detection in wireless energy transfer systems works by analyzing digital values from transmitter and receiver to detect foreign objects, comparing power loss or efficiency to expected values for accurate detection. |
16 Nov 2020 2 Citations | Object detection under IoT constraints involves partitioning, slicing, and compressing deep neural networks for efficient communication between camera-equipped IoT nodes and cloud servers, optimizing detection accuracy-communication cost trade-offs. |
| The objection detection apparatus works by using electromagnetic radiation directed at objects in its field of view, with a detector generating a signal upon detecting an object. | |
| Object detection works by obtaining an image, generating feature maps, determining region proposals, classifying into object categories, cropping corners, identifying boundaries, and mapping to the image. |
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