Abstract: 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.

Junyan Niu and Guanfang Liu, “Optimization Design of Robot Grasping Based on Lightweight YOLOv6 and Multidimensional Attention” International Journal of Advanced Computer Science and Applications(IJACSA), 16(4), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160423

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