基于改进YOLOv5的Logo检测算法

Logo detection algorithm based on improved YOLOv5

针对Logo图像背景复杂、Logo目标尺寸多变的问题,提出了一种基于YOLOv5的改进检测算法。首先,结合CBAM(Channel Block Attention Module),分别在图像通道与空间方向进行压缩,提取图像的关键信息与重要区域;然后,使用可变空洞卷积(SAC)使网络在不同尺度下自适应地调整特征图中的感受野大小,以捕获不同尺度下的物体信息,改善网络对多尺度目标的检测效果;最后,将归一化Wasserstein距离(NWD)嵌入损失函数,将边界框建模成2D的高斯分布,计算对应的高斯分布之间的相似度,更好地度量目标之间的相似性,提高对小目标的检测性能与模型鲁棒性和稳定性。实验结果表明,在数据量较小的数据集FlickrLogos-32中,改进后算法的平均精度均值(mAP@0.5)达到90.6%,比原始YOLOv5算法提升了1个百分点;在数据量较大的数据集QMULOpenLogo中,改进后算法的mAP@0.5达到62.7%,比原始YOLOv5算法提升了2.3个百分点;在针对特定类型的Logo检测集LogoDet3K中,针对3类商标改进后算法比原始算法的mAP@0.5分别提升了1.2、1.4与1.4个百分点,说明它有更好的Logo图像小目标检测能力。

To address the challenges posed by complex background and varying size of logo images,an improved detection algorithm based on YOLOv5 was proposed.Firstly,in combination with the Channel Block Attention Module(CBAM),compression was applied in both image channels and spatial dimensions to extract critical information and significant regions within the image.Subsequently,the Switchable Atrous Convolution(SAC)was employed to allow the network to adaptively adjust the receptive field size in feature maps at different scales,improving the detection effects of objects across multiple scales.Finally,the Normalized Wasserstein Distance(NWD)was embedded into the loss function.The bounding boxes were modeled as 2D Gaussian distributions,the similarity between corresponding Gaussian distributions was calculated to better measure the similarity among objects,thereby enhancing the detection performance for small objects,and improving model robustness and stability.Compared to the original YOLOv5 algorithm:in small dataset FlickrLogos-32,the improved algorithm achieved a mean of Average Precision(mAP@0.5)of 90.6%,with an increase of 1 percentage point;in large dataset QMULOpenLogo,the improved algorithm achieved an mAP@0.5 of 62.7%,with an increase of 2.3 percentage points;in LogoDet3K for three types of logos,the improved algorithm increased the mAP@0.5 by 1.2,1.4,and 1.4 percentage points respectively.Experimental results demonstrate that the improved algorithm has better small object detection ability of logo images.