Real-time object detection is the task of doing object detection in real-time with fast inference while maintaining a base level of accuracy.
( Image credit: CenterNet )
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Object recognition in video is an important task for plenty of applications, including autonomous driving perception, surveillance tasks, wearable devices or IoT networks.
Ranked #4 on Video Object Detection on ImageNet VID
In this work, we propose YOLObile framework, a real-time object detection on mobile devices via compression-compilation co-design.
However, poor conditions of the wireless channel connecting the mobile devices to the edge servers may degrade the overall capture-to-output delay achieved by edge offloading.
We demonstrate that the proposed framework can serve as a methodical benchmark for analyzing adversarial behaviors and risks in real-time object detection systems.
We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search.
Ranked #2 on Instance Segmentation on COCO test-dev
In this paper, we reformulate the multi-object detection task as a problem of density estimation of bounding boxes.
Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection.
Ranked #1 on Object Detection on COCO test-dev