Meanwhile, to solve the problem of missing information and utilization in the traditional U-Net structure, we design a multiscale feature fusion module that can effectively compensate for the missing contextual information and make full use of the disjoint features. In addition, an improved local ...
Ablation experiments were conducted to evaluate the impacts of MSF and CRA block and investigate the performance of the YOLOv5s model on the NEU-DET and GC10-DET datasets. Table4displays the results of the ablation experiments. To explore the impact of MSF, it can be seen that various feat...
以下是multi-scale feature fusion的计算公式: F =Σ(Wi * Gi) 其中,F表示融合后的特征向量,Wi表示第i个尺度上特征向量的权重系数,Gi表示第i个尺度上提取的特征向量。权重系数可以根据具体情况进行调整,通常采用softmax函数进行归一化处理,以保证各尺度特征向量的权重之和为1。 在计算过程中,首先从不同尺度的...
2) Multi Branch (MB) Transformer Block: 为了保留局部语义表示,除了三个factorized attention以外还包括一个额外的卷积核。factorized attention 和efficient attention 的区别在于,efficient attention 计算 Q 和 K 的 softmax,而factorized attention 只计算 K 的 softmax: 其中ρq 和ρk 是归一化函数,可以分为缩...
self.fusion3 = Encoder_MDCBlock1(64, 4, mode='iter2') self.dense3 = nn.Sequential( ResidualBlock(128), ResidualBlock(128), ResidualBlock(128) ) self.conv16x = ConvLayer(128, 256, kernel_size=3, stride=2) self.conv4 = RDB(128, 4, 128) self.fusion4 = Encoder_MDCBlock1(...
This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In th...
Abstract In order to improve the detection accuracy of the network, it proposes multi-scale feature fusion and attention mechanism net (MFANet) based on deep learning, which integrates pyramid module and channel attention mechanism effectively. Pyramid module is designed for feature fusion in the cha...
Firstly, the FFDN applies three feature maps in the improved VGG16 (Visual Geometry Group Network 16) with a proposed resolution expansion module to achieve the same resolution of the three feature maps. Then the FFDN fuses these three feature maps by a lightweight feature fusion method. ...
Multi Branch (MB) Transformer Block 对RIPN的三个特征都做attention,然后多一个分支就做3*3的cnn,保留细节特征 对于本文中的transformer,做了高效化的处理,和之前讲的efficient transformer类似,区别就是卷积位置编码和softmax的使用位置。因为efficient attention看起来更加清晰,本文提供efficient attention的图如下: ...
and the output of the residual learning block is changed, so that the attention of the convolutional neural network can be better focused on the target area thereby enhancing the network’s extraction of key information in the feature map and improving the category discrimination ability of the mo...