Jul 6, 2023 · We focus on position information encoding to achieve accurate roadside object detection by proposing the position enhancement faster network (PEFNet).

Jul 20, 2023 · Experiments on the Rope3D and. UA-DETRAC datasets show that our model outperforms advanced YOLOv6, YOLOX, and FCOS in roadside object detection.

This work proposes the position enhancement faster network (PEFNet), a model that outperforms advanced YOLOv6, YOLOX, and FCOS in roadside object detection ...

To improve small target detection performance, a position-aware feature pyramid network (PA-PAN) is proposed to enhance position information encoding, and the ...

PEFNet: Position Enhancement Faster Network for Object Detection in Roadside Perception System ; SJR · 0.960 ; CiteScore · 9.8 ; Impact factor · 3.4 ; ISSN · 21693536.

Jul 6, 2023 · 为了解决这些问题，我们专注于位置信息编码，通过提出位置增强快速网络（PEFNet）来实现准确的路边物体检测。基于YOLOv6，在Backbone和Neck网络中引入FasterNet ...

Compared with other network models, PEFNet shows more powerful performance in both detection accuracy and model lightweight, especially in small target ...

PEFNet: Position Enhancement Faster Network for Object Detection in Roadside Perception System · AWPSO-SAA: A Time Slot Allocation Algorithm for Adaptive ...

In this paper, we propose a salient object detection framework named multi-scale feature aggregation and boundary awareness network.

PEFNet: Position Enhancement Faster Network for Object Detection in Roadside Perception System · PDT-YOLO: A Roadside Object-Detection Algorithm for Multiscale ...