双向特征金字塔网络(Bidirectional Feature Pyramid Network, BiFPN) 1. 基本概念 双向特征金字塔网络(BiFPN)是一种用于构建多尺度特征表示的网络结构,特别适用于目标检测、图像分割等计算机视觉任务。BiFPN通过引入双向跨尺度连接和加权特征融合,有效地融合了不同尺度的特征信息,提高了特征的表达能力和鲁棒性。 2. 改进传
bidirectional feature pyramid network 的改进策略 英文版 Improvement Strategies for Bidirectional Feature Pyramid Network Abstract: The Bidirectional Feature Pyramid Network (BiFPN) has emerged as a powerful architecture for object detection and segmentation tasks. Its ability to fuse features from different ...
In this paper, A Recursive Attention-Enhanced Bidirectional Feature Pyramid Network (RA-BiFPN) is proposed. Firstly, we designed the attention-enhanced bidirectional feature pyramid network (A-BiFPN) to improve the detection accuracy of the small object. The A-BiFPN is composed of bidirectional ...
This article focuses on enhancing the YOLOv5 algorithm. First, the C2f module from YOLOv8 is integrated into the backbone network to improve feature extraction capabilities. Additionally, the mamba module is introduced alongside the C2f module to create a bidirectional dense feedback network, which ...
Keywords: light-field images; occlusion removal; CSPDarknet53; bidirectional feature pyramid network (BiFPN); end-to-end learning; separable convolutional blocks; half-instance initialization network (HINet); multi-scale fusion; sparse datasets; dense datasets 1. Introduction Occlusion removal in light...
It first uses Albumentations for data augmentation, then introduces a Bidirectional Feature Pyramid Network (BiFPN) and a Similarity-Aware Activation Module (SimAM) to improve the feature discrimination and perception capabilities. Additionally, it adopts Enhanced Intersection over Union Loss (EIOU) to ...
light-field images; occlusion removal; CSPDarknet53; bidirectional feature pyramid network (BiFPN); end-to-end learning; separable convolutional blocks; half-instance initialization network (HINet); multi-scale fusion; sparse datasets; dense datasets...
Second, a three-layer bidirectional feature pyramid network (BiFPN-G) is suggested to integrate the deep feature's semantic information with the shallow feature's spatial information, thus improving the scale adaptability of the model. Third, a novel efficient channel attention (ECAM) is proposed ...