Guojun Chen^*, Tao Cui, Yongjie Hou, Huihui Li

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3969-3987, 2024, DOI:10.32604/cmc.2024.056093 - 19 December 2024

Abstract Existing glass segmentation networks have high computational complexity and large memory occupation, leading to high hardware requirements and time overheads for model inference, which is not conducive to efficiency-seeking real-time tasks such as autonomous driving. The inefficiency of the models is mainly due to employing homogeneous modules to process features of different layers. These modules require computationally intensive convolutions and weight calculation branches with numerous parameters to accommodate the differences in information across layers. We propose an efficient glass segmentation network (EGSNet) based on multi-level heterogeneous architecture and boundary awareness to balance the model performance… More >

Xin Yang^1,2, Qingling Chang^1,2, Shiting Xu³, Xinlin Liu^1,2, Yan Cui^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 573-592, 2023, DOI:10.32604/cmes.2023.023424 - 05 January 2023

Abstract Monocular depth estimation is the basic task in computer vision. Its accuracy has tremendous improvement in the decade with the development of deep learning. However, the blurry boundary in the depth map is a serious problem. Researchers find that the blurry boundary is mainly caused by two factors. First, the low-level features, containing boundary and structure information, may be lost in deep networks during the convolution process. Second, the model ignores the errors introduced by the boundary area due to the few portions of the boundary area in the whole area, during the backpropagation. Focusing More >