The deployment of deep learning models on resource-constrained embedded platforms presents significant challenges due to limited computational power, memory, and energy efficiency. To address this issue, this study proposes a novel quantization method tailored to accelerate object detection using a quantized version of the YOLOv5m model, called Q_YOLOv5m. This method reduces the model's computational complexity and memory footprint, allowing for faster inference and lower power consumption, making it ideal for real-time applications on embedded systems. This approach incorporates advanced weight and activation quantization techniques to balance performance with accuracy, dynamically adjusting precision based on hardware capabilities. The efficacy of Q_YOLOv5m was confirmed, exhibiting substantial enhancements in inference speed and a reduction in model size with negligible loss in object detection accuracy. The findings underscore the capability of Q_YOLOv5m for edge applications, including autonomous vehicles, intelligent surveillance, and IoT-based monitoring systems.

3D reconstruction from 2D inputs, especially for non-rigid objects like humans, presents unique challenges due to the significant range of possible deformations. Traditional methods often struggle with non-rigid shapes, which require extensive training data to cover the entire deformation space. This study addresses these limitations by proposing a canonical pose reconstruction model that transforms single-view depth images of deformable shapes into a canonical form. This alignment facilitates shape reconstruction by enabling the application of rigid object reconstruction techniques, and supports recovering the input pose in voxel representation as part of the reconstruction task, utilizing both the original and deformed depth images. Notably, our model achieves effective results with only a small dataset of approximately 300 samples. Experimental results on animal and human datasets demonstrate that our model outperforms other state-of-the-art methods.