# Lightweight RT-DETR with Attentional Up-Downsampling Pyramid Network
## 2 Background and Literature Review
### Recent Advances and Motivation for This Work  
On the other hand, in healthcare environments, gesture images may contain more distracting objects compared to industrial work environments, including but not limited to objects with colors similar to the hands and complex, variable occlusions. Although RT-DETR offers higher overall resolution than YOLO, it still struggles to differentiate gestures from distracting elements in healthcare settings, indicating a need for improved recognition capabilities tailored to medical contexts. Moreover, the RT-DETR model comprises a large number of parameters. To reduce deployment complexity and improve inference speed, lightweight designs have been implemented to decrease parameter size and computational load. This paper proposes a Lightweight RT-DETR with AUDPN model specifically for gesture recognition and fall detection in healthcare, using the parameter-efficient RT-DETR-R18 model from the RT-DETR series as the foundational model [55].  
The main contributions addressing these issues are as follows:  
1. By integrating the ADown downsampling module and the RepNCSPELAN4 module from YOLOv9 into the RT-DETR model's backbone, we enhanced its recognition accuracy while reducing computational complexity. This modification enables the model to be deployed on resource-constrained hardware and allows for seamless upgrades without any performance loss.
2. To address the accuracy limitations of the feature fusion network, we revisited the structure of RT-DETR R18. We identified that the original upsampling and downsampling processes in the feature fusion stages of RT-DETR R18 relied solely on basic nearest-neighbor interpolation and convolution operations. This approach led to deficiencies in feature selectivity, adaptability, and global information retention. Inspired by attention mechanisms, we developed the AdaptiveGateUpsample and AdaptiveGateDownsample modules. These modules leverage attention mechanisms to address the shortcomings of conventional upsampling and downsampling in terms of feature selectivity, adaptability, and the retention of both local and global information. This results in more precise upsampling and more effective downsampling, enhancing feature representation and model performance. Based on these modules, we constructed the Attentional Up-Downsampling Pyramid Network (AUDPN), an attention-based upsampling and downsampling pyramid structure. This architecture improves multi-scale object-detection capabilities, particularly under challenging conditions such as occlusion and complex lighting, maintaining high detection accuracy and robustness.
3. Finally, the practical value of the model was validated through experiments on gesture recognition and human anomaly detection in medical care environments. The model successfully demonstrated its utility by automatically recognizing user gestures and abnormal human postures, combined with gesture-based control and fall posture-detection alarms.  
The remainder of this paper is organized as follows: Section 2 discusses the collection and processing of image data, the structure of the Lightweight RT-DETR with AUDPN and its improved modules, and model evaluation metrics. Section 3 explains the results and discussions, including training environments, parameter settings, results from ablation studies and comparative experiments, and the effectiveness of gesture recognition and fall detection. Section 4 provides a summary, limitations, and future work.