논문명 | CAGNet: A Multi-Scale Convolutional Attention Method for 3 Glass Detection Based on Transformer |
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논문종류 | SCI |
저자 | XiaoHang Hu, Rui Gao, Seungjun Yang, Kyungeun Cho |
Impact Factor | 2.592 |
게재학술지명 | Mathematics |
게재일 | 2023.09 |
Glass plays a vital role in several fields, making its accurate detection crucial. Proper de tection prevents misjudgments, reduces noise from reflections, and ensures optimal performance in other computer vision tasks. However, the prevalent usage of glass in daily applications poses unique challenges for computer vision. This study introduces a novel convolutional attention glass segmentation network (CAGNet) predicated on a transformer architecture customized for image glass detection. Based on the foundation of our prior study, CAGNet minimizes the number of train18 ing cycles and iterations, resulting in enhanced performance and efficiency. CAGNet is built upon the strategic design and integration of two types of convolutional attention mechanisms coupled with a transformer head applied for comprehensive feature analysis and fusion. To further augment segmentation precision, the network incorporates a custom edge-weighting scheme to optimize glass detection within images. Comparative studies and rigorous testing demonstrate that CAGNet outperforms several leading methodologies in glass detection, exhibiting robustness across a di24 verse range of conditions. Specifically, the IOU metric improves by 0.26% compared to that in our previous study and presents a 0.92% enhancement over those of other state-of-the-art methods. |