实验结果表明,在添加CBAM注意力机制后,YOLOv7的目标检测精度得到了显著提升。这主要归功于CBAM模块通过增强模型对关键特征的关注,提高了模型的特征表示能力。同时,由于CBAM模块的设计轻量级,对模型的运算速度和内存消耗影响较小,使得改进后的YOLOv7在保持轻量级的同时,实现了性能的提升。 结论 通过在YOLOv7中添加CBAM...
1.3 ResBlock_CBAM CBAM结构其实就是将通道注意力信息核空间注意力信息在一个block结构中进行运用。 在resnet中实现cbam:即在原始block和残差结构连接前,依次通过channel attention和spatial attention即可。 1.4性能评价 2.Yolov5加入CBAM、GAM 2.1 CBAM加入common.py中 代码语言:javascript 复制 classChannelAttentionMod...
CBAM注意力机制 代码 在commen.py中添加CBAM模块 在yolo.py中添加CBAM模块名 在cfg文件中添加CBAM信息 因为项目需要,尝试在yolov7上加入CBAM注意力机制,看看能不能提升点性能。之前有在yolov5上添加CBAM的经验,所以直接把yolov5中的CBAM搬过来,废话不多说,直接看代码吧! CBAM注意力机制 首先,介绍一下CBAM注意力...
this paper introduces a real-time aircraft target detection algorithm for remote sensing imaging using an improved lightweight attention mechanism that relies on the You Only Look Once version 7 (YOLOv7) framework (SE-CBAM-YOLOv7). The proposed algorithm replaces the standard convolution (Conv) ...
Hence, this paper introduces a real-time aircraft target detection algorithm for remote sensing imaging using an improved lightweight attention mechanism that relies on the You Only Look Once version 7 (YOLOv7) framework (SE-CBAM-YOLOv7). The proposed algorithm replaces the standard convolution ...
本发明提供了一种基于改进CBAM注意力机制的YOLOv7道路坑洼检测方法,包括:通过城镇中的监控对道路进行俯拍,获得道路坑洼的图片数据;同时对原始的路面坑洼图片数据使用SMOTE方法结合添加CoarseDropout噪声对数据集进行处理,建立典型与非典型的道路坑洼图片数据集;构建改进的YOLOv7网络,包括在YOLOv7网络中添加改进空间注意力模...
摘要:提出了一种简单有效的注意力模块,称为瓶颈注意力模块(BAM),可以与任何前馈卷积神经网络集成。我们的模块沿着两条独立的路径,通道和空间,推断出一张注意力图。我们将我们的模块放置在模型的每个瓶颈处,在那里会发生特征图的下采样。我们的模块用许多参数在瓶颈处构建了分层注意力,并且它可以以端到端的方式与任...
SE-CBAM-YOLOv7: An Improved Lightweight Attention Mechanism-Based YOLOv7 for Real-Time Detection of Small Aircraft Targets in Microsatellite Remote Sensing... SE-CBAM-YOLOv7: An Improved Lightweight Attention Mechanism-Based YOLOv7 for Real-Time Detection of Small Aircraft Targets in ...
In view of the problem of YOLOv7 detection accuracy, a pedestrian detection model improving YOLOv7 network is proposed. First, the backbone network is downsampled using modules constructed with SPPCSPC module and CBAM to reduce the loss of fine-grained feature information. Secondly, the multiscal...
Research on intelligent detection method of pavement defects incorporating CBAM-YOLOv7 modeldoi:10.3969/j.issn.1674-8425(z).2023.11.022ZHANG YanjunSHEN PingGUO AnhuiGAO BoJournal of Chongqing University of Technology (Natural Science)