@Article{cmc.2025.066740,
AUTHOR = {Xi Li, Runpu Nie, Zhaoyong Fan, Lianying Zou, Zhenhua Xiao, Kaile Dong},
TITLE = {CMACF-Net: Cross-Multiscale Adaptive Collaborative and Fusion Grasp Detection Network},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {85},
YEAR = {2025},
NUMBER = {2},
PAGES = {2959--2984},
URL = {http://www.techscience.com/cmc/v85n2/63797},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2025.066740}
}