Feature pyramid network with multi-scale prediction fusion for real-time semantic segmentationToan Van Quyen a EnvelopeMin Young Kim a b Person EnvelopeNeurocomputing
如Figure 2所示,我们首先将backbone提取的多级特征(即多层)融合为基础特征,然后将其输入Multi-Level Feature Pyramid Network(MLFPN)中。MLFPN包含交替连接的Thinned U-shape Modules(TUM)、Feature Fusion Module(FFM)和Scale-wise Feature Aggregation Module (SFAM)。其中,TUMs和FFMs提取出更具代表性的多级多尺度特征。
2. Although feature pyramids efficiently exploit features from all the layers in the network, they are not an attractive alternative to an image pyramid for detecting very small/large objects. 3. 多尺度图像分类 本节研究的是domain shift:训练和预测的输入是不同分辨率的图像。 我们之所以进行这种分析,...
In object detection, multiscale features are necessary to deal with objects with different size. Using Feature Pyramid Network (FPN) as the backbone network is a very popular paradigm in existing object detectors, we call this paradigm FPN+. However, feature fusion of FPN is insufficient to expr...
尽管这些使用特征金子塔的目标检测器具有很好的结果,但是由于仅仅根据固有的多尺度(为目标分类任务而设计的骨干的金字塔结构)。最新的,在这个工作中,作者提出了一个方法称为多级金字塔网络(Multi-Level Feature Pyramid Network, MLFPN)来构建检测不同尺度目标更有效的金子塔。
2, Based on MLFPN, we propose a single-shot object detector:M2Det, which represents theMulti-LevelMulti-ScaleDetector. Methodology: a. Construct the base feature: We use the output of FFMv1(Feature Fusion Module v1) to construct the base feature. The size is fixed as (c=768, w=W/8...
A complex roadside object detection model based on multi-scale feature pyramid network Article Open access 08 May 2025 PTCDet: advanced UAV imagery target detection Article Open access 09 November 2024 Introduction Object detection in road scene is one of the core problems of intelligent traffic...
本文提出Multi-Level Feature Pyramid Network来搭建高效检测不同尺度目标的特征金字塔。MLFPN由FFM、TUMs以及SFAM三部分组成。其中FFMv1(Feature Fusion Module)用于混合由backbone提取的多层级特征作为基础特征;TUMs(Thinned U-shape Modules)以及FFMv2s通过基础特征提取出多层级多尺度的特征;SFAM(Scale-wise Feature Aggr...
MLFPN的默认配置包含8个TUM,每个TUM有5个striding-conv和5个Upsample操作,因此它将输出6个scale的特性。为缩小参数,只对其TUM特性的每个尺度分配256个通道,这样网络就可以在GPU上轻松训练。对于输入的大小,我们采用原始的SSD、RefineDet和RetinaNet,即, 320, 512和800。在检测阶段,我们在6个金字塔形特征中分别加入2...
The second one is detecting objects in the feature pyramid extracted from inherent layers within the network while merely taking a single-scale image. This strategy demands significantly less additional memory and computational cost than the first one, enabling deployment during both the training...