Xi Li1,2, Runpu Nie1,*, Zhaoyong Fan2, Lianying Zou2, Zhenhua Xiao2, Kaile Dong1
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2959-2984, 2025, DOI:10.32604/cmc.2025.066740
- 23 September 2025
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) More >
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