Spatial Pyramid Pooling Fast (SPPF) 解析 1. 空间金字塔池化(Spatial Pyramid Pooling, SPP)的基本概念 空间金字塔池化(SPP)是一种网络层,主要用于解决卷积神经网络(CNN)中固定大小输入的限制。在标准的CNN中,网络的输入图像通常需要被调整到固定的尺寸,这可能导致信息的丢失或畸变。SPP层通过对不同区域进行池化操...
假设其中有个proposal region对应到feature maps上的大小为(w, h, 256),输入到spatial pyramid pooling layer,SPP layer将feature maps分成4*4,2*2,1*1三个level的bin,经过max pooling后将每层feature map就变成16,4,1三个level的featurevector,因为一共有256层feature maps,所以再将每层的feature vector组合在...
In this paper, we propose a model for UAV detection called DoubleM-Net, which contains multi-scale spatial pyramid pooling-fast (MS-SPPF) and Multi-Path Adaptive Feature Pyramid Network (MPA-FPN). DoubleM-Net utilizes the MS-SPPF module to extract feature maps of multiple receptive field ...
In this paper, we propose a model for UAV detection called DoubleM-Net, which contains multi-scale spatial pyramid pooling-fast (MS-SPPF) and Multi-Path Adaptive Feature Pyramid Network (MPA-FPN). DoubleM-Net utilizes the MS-SPPF module to extract feature maps of multiple receptive field ...
First, fast spatial pyramid pooling (SPPF-G) is designed for feature fusion to enrich the spatial information of small targets. 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 ...
The Backbone module incorporates Conv, C2f, and Spatial Pyramid Pooling Fast (SPPF) components, which, respectively, enhance nonlinear expression, feature propagation, and multi-scale processing capabilities. The Neck part employs a Path Aggregation Network (PAN) [13] structure, combining the Feature...
The Spatial Pyramid Pooling-Fast (SPPF) module aims to convert arbitrary-sized feature maps into fixed-sized feature vectors, which can expand the receptive field. Extract abstract features from images of sizes 160×160160×160, 80×8080×80 and 40×4040×40. Then, the extracted features are...
CBS is Conv BN SiLU; BN refers to batch normalization; SiLU denotes sigmoid-weighted linear units; CSP means cross-stage partial; SPPF is spatial pyramid pooling fast. The YOLOv5 model is composed of four parts: input, backbone, neck, and head. First, YOLOv5’s input adopts mosaic data...
the SE module, the ECA [20] module does not reduce computation by dimensionality reduction. Instead, after global averaging pooling of the input feature map to obtain a 1 × 1 × C feature map with the global perceptual field, fast one-dimensional convolution with a kernel size ofkis ...
information Article Deep Image Similarity Measurement Based on the Improved Triplet Network with Spatial Pyramid Pooling Xinpan Yuan 1, Qunfeng Liu 2, Jun Long 2,* , Lei Hu 2 and Yulou Wang 2 1 School of Computer, Hunan University of Technology, Zhuzhou 412000, China; xpyuan@hut.edu.cn ...