Figure 1. UAD-YOLOv8 network structure diagram First, we delete the P5 feature layer in the original network structure, which is mainly considered that most of the images in the UAV view are small targets. To compensate for the effect of deleting the P5 feature layer, we add the C2f modu...
根据混合系数将随机选取的两张图像完全混合,增强了模型的泛化能力,即使目标模糊,模型也具有良好的检测效果。 2.2 Using ShuffleNetv2 as the backbone network (1) 卷积运算的 input channels 和 output channels 的数量应尽量相同;(2) 尽量减少组卷积的使用(3) 通过减少小卷积和池化的使用来降低网络碎片化的程度;(...
采用 Mosaic数据增强方法的思想是随机使用4张不同图 像,将其随机拼接成一张大的图像,可以增加训练集的多样性 图1 YOLOv8网络结构图 Fig.1YOLOv8networkstructurediagram 和难度,有助于提高目标检测模型的泛化能力. 在网络训练前,自适应锚框通过学习的方式自动计算出最 适合输入图像的锚框参数,不需要手动设置.这种...
Feature upsampling is a key operation in convolutional neural networks, which can help the network extract feature information effectively and improve the recognition accuracy. CARAFE is a lightweight universal upsampling operator, which can use adaptive and optimized recombination cores at different locatio...
Fig. 1. YOLOv8 network architecture. a) CSPDarknet53 network used by Backbone; b) FPN + PAN pyramid structure used by Neck; c) decoupled header structure used by Head. The YOLOv8 model comprises four parts: Input, Backbone, Neck, and Head. These serve as input image, feature extraction...
Figure1.YOLOv8networkdiagram 图1.YOLOv8网络图 DOI:10.12677/csa.2023.13122402401计算机科学与应用 冯晶等 其中YOLOv8的骨干网络主要通过不同通道的卷积层进行叠加,采用C2f结构来提取图像特征;颈 部网络主要将骨干网络提取出来的特征进一步处理、融合后向头部网络输出;头部网络主要做预测和边 ...
The structure of Detect_DyHead module is shown in the upper right corner of Fig. 3, and the basic schematic diagram of DyHead is shown in Fig. 7. Fig. 7 Schematic diagram of DyHead. Full size image L different-scale features are extracted from the pyramid backbone network. Scale ...
EMC-YOLOv8n network structure 图选项 下载全尺寸图像 下载幻灯片 1.2 EfficientViT网络架构 为加快肠套叠特征提取的速度,本文引入EfficientViT网络作为基线模型YOLOv8n的主干网络,其网络结构如图2所示。引入重叠补丁编码(Overlap PatchEmbed)[22]的作用是将输入图像分割成重叠的小块,有助于更好地获取肠套叠图像...
EMC-YOLOv8n network structure 图选项 下载全尺寸图像 下载幻灯片 1.2 EfficientViT网络架构 为加快肠套叠特征提取的速度,本文引入EfficientViT网络作为基线模型YOLOv8n的主干网络,其网络结构如图2所示。引入重叠补丁编码(Overlap PatchEmbed)[22]的作用是将输入图像分割成重叠的小块,有助于更好地获取肠套叠图像...
Thirdly, the network structure is refined to better capture the scale characteristics of damage in B-scan data, improving small-target detection. Finally, the model is compressed using the Layer-Adaptive Sparsity for Magnitude-Based Pruning (LAMP) technique, significantly reducing model complexity ...