A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is
Recursive Hybrid Fusion Pyramid Network for Real-Time Small Object Detection on Embedded Devices
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
This paper proposes a novel RHF-Net (Recursive Hybrid Fusion pyramid network) to solve the problem of small object detection on real-time embedded devices. Though the object detection accuracy rate is improved by a large margin with SoTA (State-of-The-Art) models, e.g., SSD, YOLO, RetinaNet, and RefineDet, they are still problematic for small object detection and inefficient on embedded systems. One novelty of the RHF-Net is a bidirectional fusion module) that allows to fuse feature maps withdoi:10.1109/cvprw50498.2020.00209 dblp:conf/cvpr/ChenHWL20 fatcat:sdcgeoz36venxcvagnwaroyqv4