Real-time object detection is the task of doing object detection in real-time with fast inference while maintaining a base level of accuracy.
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Drones or general Unmanned Aerial Vehicles (UAVs), endowed with computer vision function by on-board cameras and embedded systems, have become popular in a wide range of applications.
Our model shows significant improvements over state-of-the-art models across various visual recognition tasks, including image classification, object detection, and semantic segmentation.
#20 best model for Object Detection on PASCAL VOC 2007
As DenseNet conserves intermediate features with diverse receptive fields by aggregating them with dense connection, it shows good performance on the object detection task.
Together these two variants address the two critical use cases in efficient object detection: improving efficiency without sacrificing accuracy, and improving accuracy at real-time efficiency.
#5 best model for Real-Time Object Detection on COCO
Here we aim to learn a better architecture of feature pyramid network for object detection.
#9 best model for Real-Time Object Detection on COCO (MAP metric)
In this study, we propose an efficient architecture named PeleeNet, which is built with conventional convolution instead.
Compared to YOLOv2 on the MS-COCO object detection, ESPNetv2 delivers 4. 4% higher accuracy with 6x fewer FLOPs.
#18 best model for Semantic Segmentation on PASCAL VOC 2012
Machine learning has celebrated a lot of achievements on computer vision tasks such as object detection, but the traditionally used models work with relatively low resolution images.
As for temporal detection in real-world scenes, temporal refinement networks (TRN) and temporal dual refinement networks (TDRN) are developed by propagating the refinement information across time, where we also propose a loose refinement strategy to match object motion with the previous refinement.