# A Dense Small Object Detection Algorithm Based on a Global Normalization Attention Mechanism
## 1 Introduction
Multi-scale detection in the feature enhancement network refers to inputting feature maps of different resolutions, which are obtained by fusing the detailed and semantic information of the original image into detection heads of multiple scales. This increases the richness of information inputted into the detection heads and the number and density of anchor boxes. Finally, the results from different scales are fused to obtain the final detection results. In response to the presence of multiple and dense objects in aerial images, the input features are at different scales from the downsampled data. [PERSON] et al. proposed an FPN (Feature Pyramid Network) [14], which extracts the features from the bottom-up backbone network and performs top-down upsampling, merging the features from the backbone network with the double-upsampled features to enhance the richness of details in the features. This improves the accuracy and efficiency of object detection. However, for dense small object detection, it is necessary to further increase the amount of output feature information. Therefore, we use a high-resolution feature enhancement network to improve the resolution of the network, which adds an upsampled layer into the FPN. The overall idea of dataset preprocessing is to increase the number of small objects input into the network at the image level, thereby enhancing the objected weight of the trained detection network for small objects. The method of multiple-image stitching proposed by [PERSON] et al. randomly selects four images with similar aspect ratios in the dataset and concatenates them into one image [15], which is then resized to a predefined size and inputted into the network. This method significantly improves the proportion of small objects in the input image. The Cutmix [16] method proposed by [PERSON] et al. randomly prunes the input image locally and fills it into a randomly selected local area of the image in the dataset, thereby improving the network's detection performance for occluded objects. Due to the challenges posed by dense small objects and a high proportion of small objects in the wide field of view of an aerial photography dataset, as well as the significant occlusion in certain scenes, leading to unclear object features and substantial differences between object detection box features in the dataset and pretraining datasets, it is challenging to achieve satisfactory results using generic object detection algorithms. Therefore, this paper proposes the integration of a GNAM (Global Normalization Attention Module) into the backbone network. The input features are processed using concatenated channel and spatial attention modules, preserving the cross-dimensional information. These modules suppress the irrelevant information in the input features while activating beneficial features for classification and localization tasks, enhancing the richness of detail and semantic information in the output features. They optimize the network structure for dense small objects by redesigning the backbone network, feature enhancement network, and detection head size. The goal is to increase the amount of feature information inputted into the detection heads while increasing the number and density of anchor boxes, thereby enhancing the network's perception capability for small objects. Based on the above two points, this paper proposes a Global Normalization Attention Mechanism-based object detection algorithm GNYL (Global Normalization Attention You Only Look Once). In summary, the main contributions of this paper are as follows:  
1. A new Global Normalization Attention Mechanism is proposed, which can suppress irrelevant information in the input features, enhancing the richness of detail and semantic information in the out features;
2. We demonstrate that GNYL can efficiently handle UAV images, enhancing robustness for small object detection;
3. We propose a new small object detection algorithm, GNYL, which can be applied more efficiently in practice.