# FD-YOLO: A YOLO Network Optimized for Fall Detection
## 2 Related Works  
Recent advancements in deep learning have led to significant progress in algorithms applied to various computer vision tasks, including image classification, image segmentation, and object detection. Object detection involves both the recognition of object categories and the precise localization of these objects within an image. As foundational components of AI-driven computer vision systems, deep learning-based object detection algorithms have attracted substantial research attention. Broadly, these algorithms can be categorized into transformer-based and convolutional neural network (CNN)-based approaches, based on their underlying architectural principles. Transformer-based object detection models, such as the Detection Transformer (DETR) [22], exemplify the capabilities of transformers in visual tasks. Despite their promising performance, these models are often constrained by high computational complexity, substantial memory requirements, and extended training durations, rendering them less feasible for deployment in industrial applications with stringent efficiency requirements. Conversely, CNN-based object detection methods are well-established and are typically divided into two-stage and one-stage frameworks. Two-stage approaches, including Region-based Convolutional Neural Networks (R-CNN) [23], Mask R-CNN [24], and Faster R-CNN [25], are renowned for their high detection accuracy. These methods employ a sequential process, where candidate regions are first proposed, and then boundary box regression and object classification are performed using CNNs. While these algorithms have demonstrated effectiveness in complex detection scenarios, their intricate architectures and extended inference times pose limitations for real-time applications, such as fall detection systems, where a rapid response is critical. Following this, Fast R-CNN employed a multi-task loss function, enabling simultaneous optimization of classification and localization tasks, but still suffered from latency issues. Faster R-CNN further refined the process by introducing a Region Proposal Network (RPN) that shared convolutional features between the detection and proposal stages, streamlining the pipeline. However, the architectural complexity of two-stage models remains a challenge. One-stage object detection algorithms, such as the Single Shot Detector (SSD) [26] and the You Only Look Once (YOLO) series [27], adopt a more efficient paradigm. These models perform direct classification and localization on feature maps derived from input images, circumventing the need for separate region proposal steps. SSD achieves multi-scale object detection by leveraging feature layers of varying depths, balancing speed and accuracy comparable to Faster R-CNN, although it struggles with the detection of small objects. In contrast, the YOLO framework eliminates region proposal entirely, predicting bounding boxes and object categories concurrently across the entire image [27]. While YOLO outperforms many state-of-the-art algorithms in computational efficiency, its detection accuracy is slightly lower than that of two-stage models like Fast R-CNN. Given their reduced computational demands, one-stage algorithms are better suited for real-time applications,particularly in scenarios requiring rapid processing and lightweight model architectures. Over the years, the YOLO series has emerged as a benchmark for state-of-the-art object detection, finding widespread application in industrial and practical contexts.