Optimization Design of Robot Grasping Based on Lightweight YOLOv6 and Multidimensional Attention

To address the computational redundancy and robustness limitations of industrial grasping models in complex environments, this study proposes a lightweight capture detection framework integrating Mobile Vision Transformer (MobileViT) and You Only Look Once version 6 (YOLOv6). Three innovations are developed: 1) A cascaded architecture fusing convolution and Transformer to compress parameters; 2) A multidimensional attention mechanism combining channel-pixel dual enhancement; 3) A Pixel Shuffle-Receptive Field Block (PixShuffle-RFB) decoder enabling sub-pixel localization. Experiments demonstrate that the model achieves 0.88 detection accuracy with 66 Frames Per Second (FPS) in simulations and 90.04% grasping success rate in physical tests. The lightweight design reduces computational costs by 37% versus conventional models while maintaining 93.54% segmentation efficiency (2.85 milliseconds inference). This multidimensional attention-driven approach effectively improves industrial robot adaptability, advancing capture detection applications in high-noise manufacturing scenarios.

@article{Niu2025,
  title     = {Optimization Design of Robot Grasping Based on Lightweight YOLOv6 and Multidimensional Attention},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Junyan Niu and Guanfang Liu},
  doi       = {10.14569/IJACSA.2025.0160423},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160423}
}