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.