# FD-YOLO: A YOLO Network Optimized for Fall Detection
## 3 Materials and Methods
### Attention Module  
To enhance the performance of AI algorithms, detection capabilities are often improved by increasing the network depth, integrating multi-scale feature fusion, and adding detection branches. However, increasing the network depth inherently expands the model size, which poses a limitation for methods in which real-time detection is essential. Consequently, techniques such as attention blocks, which exploit the characteristics of feature maps, have been employed to improve accuracy without increasing the number of layers. This approach significantly enhances the performance while requiring fewer computational resources compared with traditional deep networks. Figure 1: Fall dataset. Detecting falls from images requires distinguishing between falling, sitting, and walking. However, the human body's structure makes it difficult to clearly differentiate these actions. For example, bending over can appear similar to falling. In fall scenarios, key body parts like the nose, shoulders, elbows, and ankles are crucial for distinguishing actions [32]. Falls also involve interaction with the floor, making the body's center of gravity and its angle with the floor important factors [33; 34]. This information reflects global spatial interactions in videos. In this study, it is crucial to preserve and extract these global features. A notable technique for improving performance is the convolutional block attention module (CBAM) [35]. CBAM enhances the ability of the model to focus on features by combining channel attention and spatial attention mechanisms. This allows the network to emphasize the most important regions of the input image, effectively improving the detection accuracy without the need for a more complex network architecture. The first step in the CBAM is the channel attention mechanism, which enables the object detection network to learn the relative importance of each channel. By assigning adaptive weights to individual channels, the model emphasizes feature maps that are for object recognition, while diminishing less relevant or redundant information. This selective emphasis improves the overall performance of the model by focusing its resources on the most informative data. The second step involves the spatial attention mechanism, which accentuates the positional information of specific regions within the image. This mechanism allows the network to analyze the spatial layout of an image, highlighting regions that are crucial for object detection. By focusing on the spatial arrangement, the model can more accurately detect objects of varying sizes or positions, especially in complex scenarios where distinguishing between objects and the background is challenging. The spatial attention mechanism enhances the ability of the model to learn detailed positional information, thereby improving object localization and recognition, particularly in cases of significant variability in object placement or appearance. By prioritizing the processing of more important and relevant information, this approach reduces the likelihood of missed or falsely detected objects and enhances the detection and recognition accuracy by effectively focusing on specific objects. This selective focus is particularly beneficial in challenging scenarios involving diverse backgrounds, small objects, or intricate patterns where traditional detection methods may struggle to distinguish between objects and the surrounding environment [36]. A schematic of this approach is shown in Figure 2.  
Falls occur in various forms, and effectively extracting global information from images is crucial for emphasizing the relevant features for accurate detection. In some fall scenarios, parts of the body may be occluded by objects in the image, leading to a potential loss of key fall-related features as the layers process the image. To address this limitation, we seek to improve detection accuracy by incorporating a global attention block (GAB) that enhances the existing CBAM structure by adding a global computati  
Figure 2: Attention block.