How does object detection on the edge work?
Best insight from top research papers
Object detection on the edge works by using specific models and techniques to detect objects in real-time on edge devices. One approach is to use the "You only look once v4" (YOLOv4) model, which is designed for real-time object detection on edge devices . To improve accuracy without sacrificing speed, methods such as mosaic data augmentation and Mish activation function can be implemented to increase the generalization ability of the model . Additionally, adding extra convolution layers and restructuring connections can enhance the richness of the extracted features, enabling the model to extract global features and improve accuracy . These techniques allow for efficient and accurate object detection on edge devices, making it an ideal solution for real-time applications .
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| Papers (4) | Insight |
|---|---|
| The paper discusses a method for object detection on edge devices using the YOLOv4-tiny model, which is designed to have low memory overhead and high accuracy for real-time object detection on edge devices. | |
| The paper proposes a method of object edge detection using a position-sensitive detector (PSD) in defect spot mode, which improves edge-detection sensitivity and accuracy. | |
01 Jun 2023 | The paper proposes an edge-guided attention module that models explicit boundary information to generate more discriminative features for object detection. |
1 Citations | The paper proposes an Edge-guided Representation Learning Network (ERL-Net) that uses an edge-guided attention module to model explicit boundary information, generating more discriminative features for object detection. |
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