# A Dense Small Object Detection Algorithm Based on a Global Normalization Attention Mechanism
## 1 Introduction  
Object detection algorithms and drone technology have become increasingly mature and play important roles in military and civilian domains, such as traffic management, agricultural irrigation, forest patrolling, and battlefield reconnaissance. These tasks can achieve higher accuracy and efficiency by using drones while also reducing casualties. As a result, aerial object detection has become a hot topic in computer vision. However, many problems still need to be solved for object detection in remote sensing images because of the wide image perspectives, multiple objects per image, and the large number of small objects [1]. Efficient feature extraction from the limited features of small objects in aerial images is a key to solving the above problems. Among various object detection algorithms such as Faster R-CNN [2], SSD (Single Shot MultiBox Detector) [3], and RetinaNet [4], the YOLO (You Only Look Once) [5] object detection network has gained popularity due to its excellent performance in terms of both accuracy and detection speed. It has undergone network structure and module optimization in YOLOv3 [6], YOLOv5, and YOLOv7 [7]. And currently, YOLOv8 is the latest iteration that shows significant improvements in the model's light weight and detection speed. The definition of small objects is divided into two categories: absolutely small objects with object pixels less than 32 \(\times\) 32 in the COCO dataset and relatively small objects with object size less than 10% of the image size. To improve the detection performance of the YOLO object detection network for small objects, the original network is optimized and designed according to three main aspects: adding attention mechanism modules; incorporating multi-scale detection with feature enhancement networks; and dataset preprocessing [8,9]. The attention mechanism modules weigh the input features by mining more useful information in the channel or spatial dimensions, enhancing the perception of features along both the channel and spatial dimensions. In the channel attention mechanism proposed by [PERSON] et al., the SE (Squeeze and Excitation) [10] attention mechanism compresses and excites the channel information of the input features to obtain channel weights, which are then used to weigh the input features. However, the SE attention mechanism only considers features in the channel dimension and fails to capture spatial information, making it less suitable for dense small object detection. The CBAM (Convolutional Block Attention Module) [11] attention mechanism, proposed by [PERSON] et al., combines channel and spatial attention modules in a serial manner to obtain attention weights, enhancing the utilization of spatial and channel information. However, the CBAM requires an additional fully connected layer and convolutional layer, resulting in a higher computational complexity and slower speed, requiring more hardware resources. The NAM (Normalization-Based Attention Module) [12] attention mechanism, proposed by [PERSON] et al., obtains the attention weights by concatenating channel and spatial attention modules, utilizing information from different dimensional features and then improving the attention mechanism using weight factors. However, the NAM attention mechanism fails to fully explore the spatial information in the input features, resulting in lower utilization of spatial features. Some people have already integrated the interaction between the channel and spatial information into attention mechanisms, such as ECAP-YOLO [13], proposed by [PERSON] et al., which considers the interaction between the channel and spatial information and has achieved positive results in small object detection. So, this paper proposed a GNAM (Global Normalization Attention Mechanism) considering the interaction between channel and spatial information, thereby preserving cross-dimensional information and utilizing the attention weights between channels, spatial width, and spatial height to improve efficiency.