@article{Li2025, 
author = {Xi Li and Runpu Nie and Zhaoyong Fan and Lianying Zou and Zhenhua Xiao and Kaile Dong},
title = {CMACF-Net: Cross-Multiscale Adaptive Collaborative and Fusion Grasp Detection Network},
year = {2025},
journal = {Computers, Materials & Continua},
volume = {85},
number = {2},
pages = {2959-2984},
keywords = {Robot grasp, grasp detection, convolutional neural network, vision transformer, attention mechanism},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.066740},
doi = {10.32604/cmc.2025.066740},
abstract = {With the rapid development of robotics, grasp prediction has become fundamental to achieving intelligent physical interactions. To enhance grasp detection accuracy in unstructured environments, we propose a novel Cross-Multiscale Adaptive Collaborative and Fusion Grasp Detection Network (CMACF-Net). Addressing the limitations of conventional methods in capturing multi-scale spatial features, CMACF-Net introduces the Quantized Multi-scale Global Attention Module (QMGAM), which enables precise multi-scale spatial calibration and adaptive spatial-channel interaction, ultimately yielding a more robust and discriminative feature representation. To reduce the degradation of local features and the loss of high-frequency information, the Cross-scale Context Integration Module (CCI) is employed to facilitate the effective fusion and alignment of global context and local details. Furthermore, an Efficient Up-Convolution Block (EUCB) is integrated into a U-Net architecture to effectively restore spatial details lost during the downsampling process, while simultaneously preserving computational efficiency. Extensive evaluations demonstrate that CMACF-Net achieves state-of-the-art detection accuracies of 98.9% and 95.9% on the Cornell and Jacquard datasets, respectively. Additionally, real-time grasping experiments on the RM65-B robotic platform validate the framework’s robustness and generalization capability, underscoring its applicability to real-world robotic manipulation scenarios.}
}