# FD-YOLO: A YOLO Network Optimized for Fall Detection
## 5 Discussion  
This study aimed to address several challenges encountered by existing vision-based fall detection methods. Previous research faced limitations in hardware performance, which hindered the consistent achievement of high detection accuracy and real-time processing speeds. Rapid transmission of fall events is essential for effective intervention; however, this critical aspect has not been sufficiently addressed in previous studies. Although certain approaches improve fall detection accuracy by increasing the network depth or incorporating attention mechanisms, these efforts are often constrained by CNN-based attention blocks. Specifically, such blocks have a limited ability to capture global visual information, which is crucial for handling occlusions and other complex scenarios in which parts of the body may be obscured. Consequently, previous methods struggled to maintain detection in situations involving partial visibility or intricate visual environments. In addition, the widespread availability of CCTV systems confirms that many computers are already in place, connected online, and capable of performing inference. This availability has led to the exploration of a real-time system that, compared with embedded systems, can provide more accurate fall detection by leveraging global information, even in cases of occlusions. Furthermore, previous studies often relied on single fall detection datasets, exposing limitations of the ability of the model to generalize across diverse environments. Recently, with the development of models such as the generative pretrained transformer (GPT), it has been demonstrated that transformer networks, in addition to CNN-based networks, can achieve higher accuracy in various tasks. Specifically, in vision tasks, transformer networks have demonstrated the capability to learn the structural features of an image's spatial information, leading to improved detection accuracy. However, it was also noted that these networks typically require large datasets to reach optimal performance, because they rely on extensive data to effectively capture and process global spatial information. Figure 10: FD-YOLO confusion matrix. Subsequent research has explored the application of transformer networks to existing CNN architectures in the form of attention blocks, culminating in the development of the Swin Transformer. The introduction of Swin Transformer attention significantly enhanced the ability of the network to capture global information, resulting in improved fall detection accuracy. The attention block allows the network to focus on key regions of the image, thereby enabling more effective fall detection, even in scenarios with occlusions or complex background environments. This approach addresses the limitations of traditional CNN architectures, which primarily process local information by allowing the network to learn from the overall image context. Consequently, the combination of a CNN and transformer-based attention provides a more comprehensive method for identifying falls, particularly under challenging visual conditions. Furthermore, given the importance of not only detecting falls but also identifying their precise location, an object detection network is necessary. The YOLO network, which is recognized for its strength in real-time detection and object localization, was selected as the most suitable network architecture for this task. The ability of YOLO to efficiently perform both detection and localization makes it an ideal candidate for accurately identifying falls and determining their location within a frame, thereby facilitating quicker and more effective responses in real-time scenarios. In this study, we selected YOLO-based AI networks with varying depths and compared their fall detection performance. Early versions of the YOLO network, while offering faster detection speeds, often suffered from reduced accuracy. To address this issue, we conducted a comparative analysis of the latest versions of YOLO.