α通过梯度反向传播中重新加权损失,决定了FPN中深层参与浅层学习的程度。数据集中的对象体积很小,这给FPN的各个层的学习带来了很多困难。因此,每一层的学习能力是不够的,而深层没有额外的能力来帮助浅层。换句话说,FPN中各层学习难度增加,α降低时,深层和浅层之间的供求关系发生了变化,说明各层应该更加关注这一...
Therefore, we designed the attention fusion feature pyramid network (AFFPN) specifically for small infrared target detection. Specifically, it consists of feature extraction and feature fusion modules. In the feature extraction stage, the global contextual prior information of small targets is first ...
针对这种情况提出了两个问题:1.为什么当前基于FPN的目标检测器不能很好的适用于小目标检测任务?2.应如何对他们做出改进? 本文提出了Fusion Factorα,用于控制FPN相邻层特征融合时,较深层特征的权重系数(传统FPN中相当于将其设置为1)。本文分析了FPN的工作原理,认为FPN本质上在进行多任务学习(分治思想),理想情况下,...
在使用BEVFusion框架时,可能会遇到TypeError: BEVFusion: PAFPN: **init**() got an unexpected keyword argument 'upsample_cfg'这样的错误。这个错误通常意味着在初始化PAFPN类时,传入了一个不被接受的关键字参数upsample_cfg。要解决这个问题,我们可以按照以下步骤进行: 检查代码版本:首先,确保你正在使用的BEVFus...
(DS-DetNet) and a lite fusion feature pyramid network (LFFPN) for efficient feature fusion. The new model can achieve a performance trade-off between speed and accuracy using a depthwise separable bottleneck block, a lite fusion module, and an improved SSD detection front-end. The testing ...
3.1. The Overall Structure of SV-FPN Figure 2 shows the structure of SV-FPN. Small Object Features Enhancement (SOFE) module and Variance-guided RoI Fusion (VRoIF) were conducted in FPN to improve the result of small objects. The SOFE module was added to lateral connections between 𝐶2C2...
1)KIeep the infusion flow rate stably,thus there is no danger of excessive inputs;2)Flow rate is displayed on the surface.It is simple and quick for installation or operation;3)It is more precise than normal regulators and has a range of 5-250ml/h;4)Prices a...
For the sake of improve the accuracy of dynamic gesture recognition in Human-Machine Interaction, this paper proposes a new dynamic gesture recognition method FPN-3DResNeXt, which combines two-stream three-dimensional convolutional neural network (3DResNeXt) and feature pyramid (FPN). This method ...
Feature pyramid network (FPN) improves object detection performance by means of top-down multilevel feature fusion. However, the current FPN-based methods have not effectively utilized the interlayer features to suppress the aliasing effects in the feature downward fusion process. We propose an ...
We propose a novel concept, fusion factor, to control information that deep layers deliver to shallow layers, for adapting FPN to tiny object detection. After series of experiments and analysis, we explore how to estimate an effective value of fusion factor for a particular dataset by a ...